├── 2021-CV-Surveys.md
├── 2022-CV-Surveys.md
├── 2023-CV-Surveys.md
├── 2024-CV-Survey.md
├── README.md
└── image
├── 1.md
└── 52CV1.png
/2021-CV-Surveys.md:
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1 |
2 |
3 |
[一月中下旬]
31 |
32 | - [推荐几篇近期必看的视觉综述，含GAN、Transformer、人脸超分辨、遥感等](https://mp.weixin.qq.com/s?__biz=MzUzODkxNzQzMw==&mid=2247488123&idx=1&sn=f51f3137a16e625c962705997f0daf0a&chksm=fad13d2dcda6b43b1001b8ff924f317f5fcbdbcbd41894b193823e2fcd1d2412f4c3394ebb8e&scene=21#wechat_redirect)
[一月上旬]
33 |
34 | ## 46.Object Tracking(目标跟踪)
35 | * 鱼类跟踪
36 | * [A Review of Computer Vision Technologies for Fish Tracking](https://arxiv.org/abs/2110.02551)
[2021-10-07]
本篇综述是对近十年来鱼类追踪技术的发展和应用前景的全面调研。
37 | * 多目标跟踪
38 | * [Multi-target tracking for video surveillance using deep affinity network: a brief review](https://arxiv.org/abs/2110.15674)
[2021-11-01]
视频监控中的多目标跟踪（MTT）算法综述
39 | * 视觉目标跟踪
40 | * [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://arxiv.org/abs/2112.02838)
[2021-12-07]
本调研报告基于九个跟踪基准的结果，对 90 多个 DCF 和 Siamese 跟踪器进行了系统而全面的回顾。
41 |
42 |
43 |
44 | ## 45.Continual Learning(持续学习)
45 | * [Recent Advances of Continual Learning in Computer Vision: An Overview](https://arxiv.org/abs/2109.11369)
[2021-09-24]
是对计算机视觉中持续学习的最新进展进行了全面概述，包括所使用的各种技术（regularization, knowledge distillation, memory, generative replay, parameter isolation）和这些方法所应用的各个子领域。
46 |
47 |
48 |
49 | ## 44.Open Set Recognition(开集识别)
50 | * [A Survey on Open Set Recognition](https://arxiv.org/abs/2109.00893)
[2021-09-03]
51 |
52 |
53 |
54 | ## 43.Reinforcement Learning(强化学习)
55 | * [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://arxiv.org/abs/2108.11510)
[2021-08-27]
本篇综述是对计算机视觉中的深度强化学习的最新和最先进的研究进展进行了详细调研。
56 |
57 |
58 |
59 | ## 42.Visual-and-Language(视觉语言)
60 | * 视觉语言导航
61 | * [Visual-and-Language Navigation: A Survey and Taxonomy](https://arxiv.org/abs/2108.11544)
[2021-08-27]
62 |
63 |
64 |
65 |
66 | ## 41.SLAM/AR/robotics(机器人)
67 | * [From SLAM to Situational Awareness: Challenges and Survey](https://arxiv.org/abs/2110.00273)
[2021-10-04]
本篇综述是对机器人算法中从 SLAM 到 Situational Awareness（态势感知）进展的全面调研
68 | * try-on
69 | * [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://arxiv.org/abs/2111.00905)
[2021-11-02]
本篇综述对机器学习、计算机视觉和人工智能（AI）在时尚中的应用进行全面回顾。将 580 多篇相关文章归类为 22 个定义明确的时尚相关任务，以及一份 86 个公共时尚数据集的清单。
70 |
71 |
72 |
73 | ## 40.Adversarial Learning(对抗学习)
74 | * [Threat of Adversarial Attacks on Deep Learning in Computer Vision: Survey II](https://arxiv.org/abs/2108.00401)
[2021-08-03]
本篇综述是《Threat of adversarial attacks on deep learning in computer vision: A survey》的续篇，主要关注 2018 年以来对抗攻击对深度学习影响领域的进展。
75 |
76 |
77 |
78 | ## 39.Image Captioning(图像字幕)
79 | * [From Show to Tell: A Survey on Image Captioning](https://arxiv.org/abs/2107.06912)
[2021-07-16]
本次工作的目的是提供一个从视觉编码和文本生成到训练策略、使用的数据集和评估指标的图像字幕方法的全面概述和分类。
80 | * [A Thorough Review on Recent Deep Learning Methodologies for Image Captioning](https://arxiv.org/abs/2107.13114)
[2021-07-29]
本篇综述对 UpDown、OSCAR、VIVO、Meta Learning 和一个使用条件生成对抗网的模型等近期方法进行了回顾。得出尽管基于 GAN 的模型取得了最高分，但 UpDown 代表了图像字幕的一个重要基础，而由于使用了新的目标字幕的 OSCAR 和 VIVO 则更有用。
81 |
82 |
83 |
84 | ## 38.Image Synthesis(图像合成)
85 | * [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://arxiv.org/abs/2107.04240)
[2021-07-12]
本篇综述是对给定直观用户输入的图像合成方面工作的调研，包括输入通用性、图像生成方法、基准数据集和评价指标方面的进展调研。
86 | * [Multimodal Image Synthesis and Editing: A Survey](https://arxiv.org/abs/2112.13592)
:star:[code](https://github.com/fnzhan/MISE)
[2021-12-28]
多模态图像合成编辑综述
87 | * Person Generation
88 | * [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://arxiv.org/abs/2109.02081)
[2021-09-07]
本篇综述对 deep person generation 的最新进展、相关的 200 多篇论文进行了回顾，涵盖三个主要任务：talking-head generation (face), pose-guided person generation (pose) and garment-oriented person generation (cloth)，以及对 virtual fitting, digital human, generative data augmentation 一些应用进行研究。
89 |
90 |
91 |
92 | ## 37.Affective Image Content Analysis(情感图像内容分析)
93 | * [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://arxiv.org/abs/2106.16125)
[2021-07-01]
本篇综述，作者对近二十年来 AICA 的发展进行了全面调研，特别是针对以下三个主要挑战：the affective gap、perception subjectivity以及标签噪声和缺失，并重点介绍最先进的方法。
94 |
95 |
96 |
97 | ## 36.Computational Photography(光学、几何、光场成像、计算摄影)
98 | * Hyperspectral imaging(高光谱成像)
99 | * [Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey](https://arxiv.org/abs/2106.15944)
[2021-07-01]
本篇综述对这些来自广泛的 RGB 图像的最先进的光谱重建方法进行了深入调查。通过对超过 25 种方法的系统研究和比较，发现大多数数据驱动的深度学习方法尽管速度较低，但在重建精度和质量方面都优于基于先验的方法。作者还称本篇综述可以作为同行研究者的一个富有成效的参考资料，从而进一步激发相关领域的未来发展方向。
100 | * OCT
101 | * [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://arxiv.org/abs/2109.02322)
[2021-09-07]
本篇综述总结了当前在 OCT 中自动分割 ONH 的先进技术。使用 PubMed 和Scopus 进行了系统的回顾。也包括其他数据库（IEEE、Google Scholar和ARVO IOVS）中的其他作品在内，总共有 27 项审查研究。
102 |
103 |
104 |
105 | ## 35.Interest Point Detection(兴趣点检测)
106 |
107 | * [Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review](https://arxiv.org/abs/2106.07929)
[2021-06-16]
本篇综述对用于兴趣点检测的图像特征信息（IFI）提取技术进行了全面调研。
108 |
109 |
110 |
111 | ## 34.Graph Neural Networks(图神经网络)
112 | * [Survey of Image Based Graph Neural Networks](https://arxiv.org/abs/2106.06307)
[2021-06-14]
113 |
114 |
115 |
116 | ## 33.Data Augmentation(数据增强)
117 | * [Survey: Image Mixing and Deleting for Data Augmentation](https://arxiv.org/abs/2106.07085)
[2021-06-15]
118 |
119 |
120 |
121 | ## 32.Adversarial Example Detection(对抗性示例检测)
122 | * [Adversarial Example Detection for DNN Models: A Review](https://arxiv.org/abs/2105.00203)
[2021-05-04]
本文试图为 AE 检测方法提供一个理论和实验回顾。对这些方法进行了详细的讨论，并在四个数据集的不同场景下介绍了八个最先进的检测器的实验结果。还提供了该研究方向的潜在挑战和未来前景。
123 |
124 |
125 |
126 | ## 31.Change Detection(变化检测)
127 | * [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://arxiv.org/abs/2105.01342)
[2021-05-05]
用于变化检测的深度学习框架的经验性回顾:模型设计、实验框架、挑战和研究需求
128 |
129 |
130 |
131 | ## 30.Gaze Estimation(视线估计)
132 | * [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://arxiv.org/abs/2104.12668)
[2021-04-27]
:star:[code](http://phi-ai.org/GazeHub/)
本片综述是对基于外观的深度学习的视线估计方法进行的全面回顾。并从四个角度讨论这些方法：深度特征提取、深度神经网络架构设计、个人校准以及设备和平台。
133 | * [Automatic Gaze Analysis: A Survey of DeepLearning based Approaches](https://arxiv.org/abs/2108.05479)
[2021-08-13]
基于深度学习的自动眼动分析方法的调研
134 |
135 |
136 |
137 | ## 29.图像标注
138 | * [A survey of image labelling for computer vision applications](https://arxiv.org/abs/2104.08885)
[2021-04-20]
图像标注在计算机视觉中的应用调研
139 |
140 |
141 |
142 | ## 28.3D(三维视觉)
143 | * Depth Estimation(深度估计)
144 | * [Survey on Semantic Stereo Matching / Semantic Depth Estimation](https://arxiv.org/abs/2109.10123)
[2021-09-22]
145 | * [Single Image Depth Estimation: An Overview](https://arxiv.org/abs/2104.06456)
[2021-04-15]
本文是对场景理解中的重要子任务深度估计的回顾，并重点关注单图像深度估计。从早于深度学习，利用手工制作的特征和假设的非深度学习方法，到大多使用深度学习技术的最新作品；从监督到无监督。以及将深度估计问题与语义分割和表面法线估计等相关任务相结合的多任务方法。最后，还讨论了对当代解决方案的机制、原理和失败案例的调查。
146 |
147 |
148 |
149 | ## 27.Sign Language Production(手语制作)
150 | * [Sign Language Production: A Review](https://arxiv.org/abs/2103.15910)
[2021-03-31]
本文回顾了利用深度学习在手语制作（SLP）和相关领域的最新进展。旨在简要总结SLP 的最新成就，讨论其优势、局限性和未来的研究方向。
151 |
152 |
153 |
154 | ## 26.Image Representation(图像表征)
155 |
156 | * [A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation](https://arxiv.org/abs/2103.14799)
[2021-03-30]
本文是对用于图像表征的正交矩进行的全面调查，涵盖快速/精准计算、鲁棒性/不变性优化和定义扩展方面的最新进展。为各种广泛使用的正交矩创建了一个软件包，并在同一基础上对这些方法进行了评估。作者表示所提出的理论分析、软件实现和评价结果可以为社会提供支持，特别是在开发新技术和推广实际应用方面。
157 |
158 |
159 |
160 |
161 | ## 25.Multimedia Technology(多媒体技术)
162 |
163 | * [A Survey of Multimedia Technologies and Robust Algorithms](https://arxiv.org/abs/2103.13477)
[2021-03-26]
本文是对从日常生活到医学研究的各种多媒体技术和强大算法的调研。
164 | * [Multimedia Technology Applications and Algorithms: A Survey](https://arxiv.org/abs/2104.01301)
[2021-04-06]
多媒体技术及用于综述调研
165 |
166 |
167 |
168 | ## 24.Image Processing(图像处理)
169 | * 图像美学评级
170 | * [A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment](https://arxiv.org/abs/2103.11616)
[2021-03-23]
文章是对近期图像美学自动评估技术进行的文献调查。回顾大量的传统手工制作和基于深度学习的方法。并对关键的问题进行讨论，如为什么一些特征或模型比其他的表现更好，有什么局限性。最后对不同方法的量化结果进行比较。
171 | * 图像去雾
172 | * [A Comprehensive Survey on Image Dehazing Based on Deep Learning](https://arxiv.org/abs/2106.03323)
[2021-06-08]
对基于深度学习的图像去雾算法技术研究调研
173 | * Image Composition(图像合成)
174 | * [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://arxiv.org/abs/2106.14490)
[2021-06-29]
:star:[code](https://github.com/bcmi/Awesome-Image-Composition)
本篇综述对图像合成进行了全面调研。
175 | * Image Representation(图像表示)
176 | * [A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation](https://arxiv.org/abs/2103.14799)
[2021-08-27]
:star:[code](https://github.com/ShurenQi/MomentToolbox)
177 | * 图像增强
178 | * [A Survey on Deep learning based Document Image Enhancement](https://arxiv.org/abs/2112.02719)
[2021-12-07]
基于深度学习的文档图像增强综述调研
179 |
180 |
181 |
182 | ## 23.Semantic Scene Completion(语义场景完成SSC)
183 |
184 | - [3D Semantic Scene Completion: a Survey](https://arxiv.org/abs/2103.07466)
[2021-03-15]
本文是对当代最先进的 3D 语义场景完成方法进行的全面调查。回顾并严格分析了所提出的方法的主要方面，包括需要考虑的重要设计选择，并比较了它们在流行的 SSC 数据集中的性能。作者希望这项调查将支持该领域的进一步发展，旨在提供新的见解，并帮助没有经验的读者浏览该领域。
185 |
186 |
187 |
188 | ## 22.Image Segmentation(图像分割)
189 |
190 | - [Deep Learning based 3D Segmentation: A Survey](https://arxiv.org/abs/2103.05423)
[2021-03-10]
本篇综述是对基于深度学习的三维分割的最新进展进行了全面的调查，包含150多篇论文文献。总结了最常用的 pipelines，对其亮点和不足进行了讨论，并分析了这些分割方法的竞争结果。还在分析的基础上，提供了未来有前景的研究方向。
191 | * 视频分割
192 | * [A Survey on Deep Learning Technique for Video Segmentation](https://arxiv.org/abs/2107.01153)
[2021-07-05]
本篇综述是首篇对视频分割领域最新进展的调查报告，包含 AVOS、SVOS、IVOS、LVOS、VSS、VIS 和 VPS 几个子领域。
193 | * 全景分割
194 | * [Panoptic Segmentation: A Review](https://arxiv.org/abs/2111.10250)
[2021-11-22]
:star:[code](https://github.com/elharroussomar/Awesome-Panoptic-Segmentation)
本次调研是对现有全景分割方法的第一次全面回顾。
195 | * 语义分割
196 | * [Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey](https://arxiv.org/abs/2112.03241)
[2021-12-07]
本调查报告是对无监督域适应在语义分割任务中快速发展的五年来的努力总结，包含了语义分割本身的重要性以及使分割模型适应新环境的关键需求。
197 |
198 |
199 |
200 | ## 21.Few/Weak/Zero-Shot Learning,Domain Generalization/Adaptation(小/弱/零样本学习，域适应，域泛化)
201 |
202 | * 弱样本学习
203 | * [Weak Novel Categories without Tears: A Survey on Weak-Shot Learning](https://arxiv.org/abs/2110.02651)
:star:[code](https://github.com/bcmi/Awesome-Weak-Shot-Learning)[2021-03-05]
弱样本学习综述
204 | * 域泛化
205 | * [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://arxiv.org/abs/2103.03097)
[2021-03-05]
本篇文章作者从 same-source（同源） 和 cross-source（跨源）域对点云配准进行了全面的回顾
206 | * [Domain Generalization: A Survey](https://arxiv.org/abs/2103.02503)
[2021-03-04]
本篇文章是首次对 DG（Domain Generalization）的十年发展进行了全面的文献回顾总结
207 | * 域适应
208 | * [A Survey on Deep Domain Adaptation for LiDAR Perception](https://arxiv.org/abs/2106.02377)
[2021-06-07]
本篇综述是对域适应方法的最新进展的调研，并提出专门针对 LiDAR 感知的有趣研究问题。
209 | * [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://arxiv.org/abs/2112.06745)
[2021-12-14]
无监督域适应在视觉识别应用综述
210 |
211 |
212 |
213 | ## 20.Anomaly Detection(异常检测)
214 |
215 | - [Image/Video Deep Anomaly Detection: A Survey](https://arxiv.org/abs/2103.01739)
216 |
[2021-03-03]
基于图像和视频的深度学习 AD 的专业综述的缺失，作者对此进行了深入调查。工作的重点在无监督技术上，并提供 AD 概念的精确定义，同时对最近提出的 AD 方法进行了的分类。以及对当前的挑战和未来的研究方向进行了彻底讨论。
217 | * [Visual Anomaly Detection for Images: A Survey](https://arxiv.org/abs/2109.13157)
[2021-09-28]
本篇综述对文献中经典的和基于深度学习的视觉异常检测方法进行了全面调研。根据相关方法的基本原理对其进行分组，并仔细讨论其假设、优势和劣势。旨在帮助研究人员了解视觉异常检测方法的共同原理，并确定该领域有前途的研究方向。
218 | * [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://arxiv.org/abs/2110.14051)
[2021-10-28]
OOD检测、OSR、ND（单类学习）和AD综述调研
219 | * OOD
220 | * [Generalized Out-of-Distribution Detection: A Survey](https://arxiv.org/abs/2110.11334)
[2021-10-22]
:star:[code](https://github.com/Jingkang50/OODSurvey)
提出一个通用框架，generalized OOD detection，包含AD、ND、OSR、OD检测和OD，并进行调研。
221 | * 视频异常检测
222 | * [Multimedia Datasets for Anomaly Detection: A Survey](https://arxiv.org/abs/2112.05410)
[2021-12-13]
本文基于异常检测的应用，对各种视频、音频以及视听数据集进行了全面调查。旨在解决缺乏基于异常检测的多媒体公共数据集的综合比较和分析的问题。
223 |
224 |
225 |
226 | ## 19.Transformers
227 |
228 | - [Transformers in Vision: A Survey](https://arxiv.org/abs/2101.01169)
[2021-01-01]
旨在为计算机视觉学科中的 Transformer 模型提供一个全面的概述
229 | * [A Survey of Visual Transformers](https://arxiv.org/abs/2111.06091)
[2021-11-12]
本篇综述对应用于各种视觉任务，包括分类、检测和分割的一百多个Transformer模型进行全面回顾。
230 | * VL
231 | * [Survey: Transformer based Video-Language Pre-training](https://arxiv.org/abs/2109.09920)
[2021-09-22]
232 |
233 |
234 |
235 | ## 18.Point Clouds(点云)
236 |
237 |
238 | - [Attention Models for Point Clouds in Deep Learning: A Survey](https://arxiv.org/abs/2102.10788)
[2021-02-23]
对使用注意力模型的点云特征表示进行全面的概述
239 | - [A comprehensive survey on point cloud registration](https://arxiv.org/abs/2103.02690)
[2021-03-05]
回顾了跨源点云配准的发展，并建立一个新的基准来评估最新的配准算法。此外，本次综述还总结了基准数据集，并讨论了各领域的点云配准应用。
240 |
241 |
242 |
243 | ## 17.Object Detection(目标检测)
244 |
245 | - [Occlusion Handling in Generic Object Detection: A Review](https://arxiv.org/abs/2101.08845)
[2021-01-25]
246 | - [A Survey of Deep Learning Techniques for Weed Detection from Images](https://arxiv.org/abs/2103.01415)
[2021-03-03]
从图像中检测杂草的深度学习技术调查报告,共梳理了70篇相关文献。
247 | - [A Survey of Modern Deep Learning based Object Detection Models](https://arxiv.org/abs/2104.11892)
[2021-04-27]
本片综述是对基于深度学习的目标检测器最新发展的调查。提供了检测中使用的基准数据集和评估指标的简要概述，以及识别任务中使用的一些突出的骨干架构。涵盖了在边缘设备上使用的当代轻量级分类模型。并比较了这些架构在多个指标上的表现。
248 | - [Unsupervised Domain Adaption of Object Detectors: A Survey](https://arxiv.org/abs/2105.13502)
[2021-05-31]
本篇综述对深度目标检测器的无监督域适应性任务的现有方法进行了广泛调查。详细回顾了过去几年中发表的总共五十种方法。
249 | * 弱监督目标定位与检测
250 | * [Weakly Supervised Object Localization and Detection: A Survey](https://arxiv.org/abs/2104.07918)
[2021-04-19]
本篇综述回顾了弱监督目标定位与检测的经典模型，来自现成深度网络的特征表示方法，完全基于深度学习的方法，以及在该领域广泛使用的公开数据集和标准评估指标。还该领域的主要挑战、发展历史、各类方法的优/劣势、不同类别方法之间的关系、应用以及未来可能的发展方向进行了讨论，以进一步促进该研究领域的发展。
251 | * [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://arxiv.org/abs/2105.12694)
[2021-05-27]
本篇综述总结了大量的深度学习 WSOD 方法，以及对所面临挑战提出了解决方案。
252 | * 微生物检测
253 | * [A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: from Traditional Image Processing and Classical Machine Learning to Current Deep Convolutional Neural Networks and Potential Visual Transformers](https://arxiv.org/abs/2105.03148)
[2021-05-10]
本篇综述总结了从 1985 年至今的 137 篇相关技术论文。按照时间顺序对现有的微生物检测方法，从传统的图像处理和传统的机器学习到深度学习方法进行了分析。并介绍一些潜在的方法，包括 visual transformers。有助于研究人员对微生物检测领域的发展过程、研究现状和未来趋势有更全面的了解，为其他领域的研究人员提供参考。
254 | * 显著目标检测
255 | * [Salient Objects in Clutter](https://arxiv.org/abs/2105.03053)
[2021-05-10]
本篇综述涉及 348 个参考文献，201个模型的调研，以及100个基准模型。发现并解决了 SOD 中长期被忽视的数据选择偏差问题。与以前的研究不同，本次目标是在自然环境探索 SOD 任务。
256 | * 小目标检测
257 | * [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://arxiv.org/abs/2107.07927)
[2021-07-19]
深度域适应和小目标检测的挑战、技术和数据集调查综述
258 | * 小样本自监督目标检测
259 | * [A Survey of Self-Supervised and Few-Shot Object Detection](https://arxiv.org/abs/2110.14711)
[2021-10-29]
本篇综述对近期关于小样本和自监督的目标检测方法进行了全面回顾。
260 | * 小样本目标检测
261 | * [A Comparative Review of Recent Few-Shot Object Detection Algorithms](https://arxiv.org/abs/2111.00201)
[2021-11-02]
本篇综述从多方面的角度全面介绍了当前在小样本目标检测方面的经典和最新成果以及未来的研究预期。
262 | * [Few-Shot Object Detection: A Survey](https://arxiv.org/abs/2112.11699)
[2021-12-23]
263 | * Low-Shot 目标检测
264 | * [A Survey of Deep Learning for Low-Shot Object Detection](https://arxiv.org/abs/2112.02814)
[2021-12-07]
265 |
266 |
267 |
268 | ## 16.Human Action Detection and Recognitionn(人体动作检测与识别)
269 |
270 | - [Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark](https://arxiv.org/abs/2101.01665)
[2021-01-05]
对近期基于可穿戴传感器的人类活动识别中的优异表现方法进行了广泛回顾
271 | - [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://arxiv.org/abs/2110.00111)
[2021-10-04]
在未修剪视频中基于深度学习的动作检测调研综述
272 |
273 |
274 |
275 | ## 15.Person Re-identification(人员重识别)
276 | - [Deep Learning for Person Re-identification: A Survey and Outlook](https://arxiv.org/abs/2001.04193v2)
[[github](github.com/mangye16/ReI)]
[2021-01-06]
277 | - [Deep Gait Recognition: A Survey](https://arxiv.org/abs/2102.09546)
[2021-02-19]
全面调查深度学习在步态识别方面的突破和最新发展，并涵盖了包括数据集、测试协议、最先进的解决方案、挑战和未来研究方向在内的广泛话题。
278 | * [Deep Learning Based Person Re-Identification Methods: A Survey and Outlook of Recent Works](https://arxiv.org/abs/2110.04764)
[2021-10-12]
本篇综述为了方便研究人员更好地了解人员重识别邻域的最新研究成果和未来发展趋势,对传统的和基于深度学习的人员重识别方法进行了全面调研。
279 | * Person Search
280 | * [Person Search Challenges and Solutions: A Survey](https://arxiv.org/abs/2105.01605)
[2021-05-05]
IJCAI 2021
本篇综述从挑战和解决方案的角度对近期关于基于图像和文本的人物搜索工作进行的调研
281 | * 无监督人员重识别
282 | * [Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions](https://arxiv.org/abs/2109.06057)
[2021-09-14]
本次综述就从挑战和解决方案的角度对近期关于无监督人员重识别的工作的进行全面调研，解决了数据的可扩展性问题。
283 |
284 |
285 |
286 | ## 14.Human Pose Estimation(人体姿态估计)
287 |
288 | - [Gesture Recognition in Robotic Surgery: a Review](https://arxiv.org/abs/2102.00027)
[2021-02-02]
机器人手术中的手势识别：综述
289 | - [Single Person Pose Estimation: A Survey](https://arxiv.org/abs/2109.10056)
[2021-09-22]
290 | * 多人人体姿态估计
291 | * [Bottom-up approaches for multi-person pose estimation and it's applications: A brief review](https://arxiv.org/abs/2112.11834)
[2021-12-23]
HPE自下而上方法的最新进展
292 |
293 |
294 |
295 | ## 13.Image Classification(图像分类)
296 |
297 | - [One-Class Classification: A Survey](https://arxiv.org/abs/2101.03064)
[2021-01-11]
单分类综述
298 | - [Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects](https://arxiv.org/abs/2101.06116)
[2021-01-18]
高光谱图像分类综述
299 | - [Deep Learning for Scene Classification: A Survey](https://arxiv.org/abs/2101.10531)
[2021-01-27]
深度学习在场景分类的应用综述
300 | - [Online Continual Learning in Image Classification: An Empirical Survey](https://arxiv.org/abs/2101.10423)
[2021-01-27]
图像分类中的在线持续学习综述
301 | * 杂草检测
302 | * [Deep Learning Techniques for In-Crop Weed Identification: A Review](https://arxiv.org/abs/2103.14872)
[2021-03-30]
文章是对深度学习技术在基于图像的杂草检测领域最新发展的调查。首先介绍了与杂草检测相关的深度学习基本原理。然后对关于深度杂草检测的进展进行回顾，以及讨论了包括公共杂草数据集在内的研究材料。最后，总结开发可实际部署的杂草检测方法所面临的挑战，以及对未来研究机会的讨论。作者希望这篇综述能对该领域进行及时的调查，并吸引更多的研究者来解决这一跨学科的研究问题。
303 | * 小样本分类
304 | * [Deep Metric Learning for Few-Shot Image Classification: A Selective Review](https://arxiv.org/abs/2105.08149)
[2021-05-19]
305 | * 长尾学习
306 | * [Deep Long-Tailed Learning: A Survey](https://arxiv.org/abs/2110.04596)
[2021-10-12]
本篇综述是对 2021 年中期之前所提出的经典深度长尾学习方法，根据分类法，即类的再平衡、信息增强和模块改进进行的全面回顾。
307 | * 细粒度
308 | * [Fine-Grained Image Analysis with Deep Learning: A Survey](https://arxiv.org/abs/2111.06119)
[2021-11-12]
对基于深度学习的细粒度图像分析（FGIA）的最新进展进行了全面调查。
309 | * 高光谱图像分类
310 | * [A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples](https://arxiv.org/abs/2112.01800)
:star:[code](https://github.com/ShuGuoJ/HSI-Classification)
[2021-12-06]
深度学习用于少数标注样本的高光谱图像分类综述
311 |
312 |
313 |
314 | ## 12.Image Retrieval(图像检索)
315 |
316 | - [Deep Image Retrieval: A Survey](https://arxiv.org/abs/2101.11282)
[2021-01-28]
图像检索综述
317 | - [Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval](https://arxiv.org/abs/2105.07391)
[2021-05-18]
318 |
319 |
320 |
321 |
322 | ## 11.Face(人脸技术)
323 |
324 | - [Fast Facial Landmark Detection and Applications: A Survey](https://arxiv.org/abs/2101.10808)
[2021-01-27]
人脸关键点检测综述
325 | - [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://arxiv.org/abs/2103.00218)
[[主页](http://www.xujuefei.com/dfsurvey)]
[2021-03-02]
对抵制恶意的 DeepFakes 综述调查
326 | * 人脸超分辨率
327 | * [Deep Learning-based Face Super-resolution: A Survey](https://arxiv.org/abs/2101.03749)
[2021-01-12]
人脸超分辨率也称为【facial hallucination人脸幻构】系统地对人脸超分辨率中的深度学习技术进行了全面的回顾。
328 | * 人脸识别检测
329 | * [About Face: A Survey of Facial Recognition Evaluation](https://arxiv.org/abs/2102.00813)
[2021-02-02]
人脸识别综述
330 | * [Going Deeper Into Face Detection: A Survey](https://arxiv.org/abs/2103.14983)
[2021-03-30]
本文是对近期基于深度学习的人脸检测方面文献的综述，包括五十多种人脸检测方法。并对这些方法的不同方面进行了全面的评述，包括训练数据、网络结构的选择、损失函数、训练策略以及它们贡献。作者根据对人脸检测的贡献技术将这些方法分为以下几个架构组：1) Cascade-CNN Based Models 2) R-CNN and Faster-RCNN Based Models 3) Single Shot Detector Models 4) Feature Pyramid Network Based Models 5) Other models 。还总结一些流行的人脸检测基准，如 Wider-Face、FDDB 和 PASCAL Face，以及在这些流行基准上的量化性能。最后，对未来几年基于深度学习的人脸检测的一些公开挑战和有前途的方向进行了讨论。
331 | * [Performance analysis of facial recognition: A critical review through glass factor](https://arxiv.org/abs/2104.01536)
[2021-04-06]
本篇综述针对 glass factor 对人脸识别影响的全面调研。
332 | * [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://arxiv.org/abs/2110.05044)
[2021-10-12]
本文介绍了在基于神经网络的人脸识别系统中保护人脸模板的生物识别模板保护（BTP）方法的调查。
333 | * [Detect Faces Efficiently: A Survey and Evaluations](https://arxiv.org/abs/2112.01787)
[2021-12-06]
334 | * 3D 人脸识别
335 | * [3D Face Recognition: A Survey](https://arxiv.org/abs/2108.11082)
[2021-08-26]
对近十年基于深度学习的3D人脸识别调研，主要集中在人脸增强、数据预处理和网络架构方面。
336 | * 人脸表情识别
337 | * [Weakly Supervised Learning for Facial Behavior Analysis : A Review](https://arxiv.org/abs/2101.09858)
[2021-01-26]
无监督学习在人脸表情识别的应用综述
338 | * 微表情识别
339 | * [Deep Learning based Micro-expression Recognition: A Survey](https://arxiv.org/abs/2107.02823)
[2021-07-08]
本篇综述是对基于深度学习的微表情识别进展的调研，包括数据集、深度MER管道和最有影响力的方法的 bench-marking。为该领域定义了一个新的分类法，包含了基于 DL 的 MER 的所有方面，并对于每个方面，都总结和讨论了基本方法和先进的发展。作者称这是首个对深度 MER 方法的调查，可以作为未来 MER 研究的一个参考点。
340 | * 面部情感分析FAA
341 | * [Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges](https://arxiv.org/abs/2103.15599)
[2021-03-30]
本文是对基于 Graph 的面部情感分析的全面调查，包括算法的演变及其应用。首先，介绍情感分析的背景知识，特别是关于 Graph（图）的作用。然后，对文献中广泛用于基于图的情感表示的方法进行讨论，并展示出图构建的趋势。对于基于图的情感分析中的关系推理，作者根据传统方法或深度模型的使用情况对现有研究进行分类，特别强调最新的图神经网络。还总结了标准 FAA 问题上最先进的实验比较。最后，将综述扩展到当前的挑战和潜在的方向。作者称是首次对基于图的 FAA 方法的调查，该研究结果可以作为该领域未来研究的参考点。
342 | * DeepFake 检测
343 | * [Deep Fake Detection: Survey of Facial Manipulation Detection Solutions](https://arxiv.org/abs/2106.12605)
[2021-06-25]
:star:[code](https://github.com/sagarmandiya/DeepFake-Detection)
344 | * 人脸活体检测
345 | * [Deep Learning for Face Anti-Spoofing: A Survey](https://arxiv.org/abs/2106.14948)
[2021-06-30]
:star:[code](https://github.com/ZitongYu/DeepFAS)
本篇综述对基于深度学习的 FAS 的最新进展进行了全面调研。包含方法、数据集、应用等方面的比较探讨。
346 |
347 |
348 |
349 |
350 | ## 10.Image Super-resolution(图像超分辨率)
351 |
352 | - [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://arxiv.org/abs/2102.09351)
[2021-02-19]
基于深度学习的单图像超分辨调查
353 | - [Real-World Single Image Super-Resolution: A Brief Review](https://arxiv.org/abs/2103.02368)
[2021-03-04]
真实单图像超分辨率综述
354 | * [Advancing biological super-resolution microscopy through deep learning: a brief review](https://arxiv.org/abs/2106.13064)
[2021-06-25]
本篇综述是对近期在使用深度学习来提高超分辨率显微镜性能方面进展的调研。主要关注深度学习如何促进超分辨率图像的重建，以及对相关的关键技术挑战进行讨论。
355 | * [From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution](https://arxiv.org/abs/2109.14335)
:star:[code](https://github.com/CV-JunchengLi/SISR-Survey)
[2021-09-30]
本篇综述根据其目标对基于 DL 的单幅图像超分辨率方法进行了全面调研，包括重建效率、重建精度、感知质量以及其他可以进一步提高模型性能的技术。
356 | * 盲图像 SR
357 | * [Blind Image Super-Resolution: A Survey and Beyond](https://arxiv.org/abs/2107.03055)
[2021-07-08]
本篇综述是对盲图像 SR 最新进展的系统回顾
358 |
359 |
360 |
361 | ## 9.Quantization/Pruning/Knowledge Distillation/Model Compression(量化、剪枝、蒸馏、模型压缩/扩展与优化)
362 |
363 | - [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://arxiv.org/abs/2101.09671)
[2021-01-26]
量化剪枝综述
364 | - [Dynamic Neural Networks: A Survey](https://arxiv.org/abs/2102.04906)
[2021-02-09]
解读：[【深度】清华黄高等人新作：动态神经网络首篇综述](https://mp.weixin.qq.com/s/aEj1JfkpnsXB4ZRxeWfZAQ)
365 |
366 |
367 |
368 | ## 8.Deep Learning(深度学习)
369 |
370 | - [Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion](https://arxiv.org/abs/2101.08301)
[2021-01-22]
基于深度学习、人工智能、机器学习的时尚穿搭技术综述
371 | * 神经网络
372 | * [A Survey of Quantization Methods for Efficient Neural Network Inference](https://arxiv.org/abs/2103.13630)
[2021-03-26]
用于高效神经网络推理的量化方法研究
373 | * [Hyperbolic Deep Neural Networks: A Survey](https://arxiv.org/abs/2101.04562)
[2021-01-13]
374 | * Attention(注意力机制)
375 | * [Attention, please! A survey of Neural Attention Models in Deep Learning](https://arxiv.org/abs/2103.16775)
[2021-04-01]
为了评估注意力在深度神经网络中的应用广度，作者在本次调查中对该领域进行了系统的回顾。包括该领域的数百种架构，确定并讨论一些表现出重大影响的架构。还开发并公开一种自动化的方法，以促进该领域评论的发展。通过对 650 部作品进行批判性分析，描述了注意力在卷积、循环网络和生成模型中的主要用途，并确定共同的用途和应用子群。此外，还描述了注意力在不同应用领域的影响，以及它们对神经网络可解释性的影响。最后，列出可能的趋势和进一步研究的机会，希望这篇综述能对该领域的主要注意力模型进行简洁的概述，并指导研究人员开发未来的方法，以推动进一步的改进。
376 | * [Attention mechanisms and deep learning for machine vision: A survey of the state of the art](https://arxiv.org/abs/2106.07550)
[2021-06-15]
本篇综述是对基于 Attention（注意力）机制和深度学习在各种机器视觉（MV）任务/应用中的合并的研究调研。包含 110 多篇论文作为研究参考。
377 | * [Attention Mechanisms in Computer Vision: A Survey](https://arxiv.org/abs/2111.07624)
[2021-11-16]
本篇综述系统地回顾和总结了计算机视觉中深度神经网络的注意机制。
378 | * 集成学习
379 | * [Ensemble deep learning: A review](https://arxiv.org/abs/2104.02395)
[2021-04-07]
本篇综述是对目前最先进的深度集成模型的调研，为研究学者提供一个广泛的总结。集成模型大致可分为 bagging, boosting 和 stacking，基于 negative correlation 的深度集成模型，显式/隐式合集，同质/异质合集，决策融合策略，无监督、半监督、强化学习和基于在线/增量、多标签的深度集成模型。此外，还深度集成模型在不同领域的应用进行了简要讨论。并在本文的最后提出一些未来的建议和研究方向。
380 |
381 |
382 |
383 | ## 7.Aeria/Drones/Satellite/RS Image(航空影像/无人机)
384 |
385 | - [A Review on Deep Learning in UAV Remote Sensing](https://arxiv.org/abs/2101.10861)
[2021-01-29]
深度学习在无人机遥感中的应用综述
386 | * 遥感图像分类
387 | * [A survey of active learning algorithms for supervised remote sensing image classification](https://arxiv.org/abs/2104.07784)
[2021-04-19]
主动学习算法在监督式遥感图像分类中的应用研究.本篇综述回顾并测试了主动学习算法的主要系列：committee, large margin 和 posterior probability-based。并对每一种算法，都讨论了遥感界的最新进展，以及详细介绍和测试了一些启发式算法。考虑了几个具有挑战性的遥感场景，包括非常高的空间分辨率和高光谱图像分类。最后，为新用户和/或没有经验的用户提供了选择良好架构的指南。
388 | * 检测与跟踪
389 | * [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://arxiv.org/abs/2110.12638)
[2021-10-26]
本文是对基于 DL 的无人机目标检测和跟踪方法的研究进展和前景进行了全面调查。
390 |
391 |
392 |
393 | ## 6.GAN(生成对抗网络)
394 |
395 | - [GAN Inversion: A Survey](https://arxiv.org/abs/2101.05278)
[2021-01-15]
[[awesome gan-inversion papers](https://github.com/weihaox/awesome-image-translation/blob/master/awesome-gan-inversion.md)]
[[Papers on generative modeling](https://github.com/zhoubolei/awesome-generative-modeling)]
GAN 逆映射综述
396 | - [Adversarial Text-to-Image Synthesis: A Review](https://arxiv.org/abs/2101.09983)
[2021-01-26]
文本到图像生成综述
397 | - [Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy](https://arxiv.org/abs/1906.01529)
[2021-03-29]
:star:[code](https://github.com/sheqi/GAN_Review):newspaper:[Publication](https://dl.acm.org/doi/fullHtml/10.1145/3439723)
398 | * [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://arxiv.org/abs/2105.01800)
[2021-05-06]
本篇综述对基于 GAN 驱动的快速 MRI 方法进行了回顾，并对各种解剖数据集进行了比较研究，以证明这种快速 MRI 的通用性和鲁棒性，同时提供了未来的展望。
399 | * [GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network](https://arxiv.org/abs/2108.03857)
[2021-08-10]
使用生成式对抗网络进行视觉艺术、音乐和文学文本生成的调研
400 | * [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://arxiv.org/abs/2110.01442)
[2021-10-05]
提供了 GANs 在从天文学和生物学等 STEM 领域，到市场营销和金融等商业领域，再到音乐等艺术领域等等的12个领域应用的最全面的调研。
401 | * 图像合成
402 | * [A Survey on Adversarial Image Synthesis](https://arxiv.org/abs/2106.16056)
[2021-01-01]
本篇综述提供一个用于图像合成的方法分类，并对文本到图像合成和图像到图像翻译的不同模型进行了全面回顾，以及对一些评估指标和用 GAN 进行图像合成的未来可能研究方向的讨论。
403 | * 图像到图像翻译
404 | * [Comparison and Analysis of Image-to-Image Generative Adversarial Networks: A Survey](https://arxiv.org/abs/2112.12625)
[2021-12-24]
405 |
406 |
407 |
408 | ## 5.智能驾驶
409 | - [Vision-based Vehicle Speed Estimation for ITS: A Survey](https://arxiv.org/abs/2101.06159)
[2021-01-18]
车速估计综述
410 | * 自动驾驶
411 | * [Explainability of vision-based autonomous driving systems: Review and challenges](https://arxiv.org/abs/2101.05307)
[2021-01-15]
自动驾驶综述
412 | * [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://arxiv.org/abs/2104.01789)
[2021-04-06]
本篇综述对各种针对基于深度学习的 ADS pipeline 的攻击进行了详细的回顾和分析。全面阐明了基于深度学习的 ADS 中最先进的攻击和防御方法。并提出未来应用新攻击的研究方向，以及保障和提高基于深度学习的 ADS 的鲁棒性。
413 | * [Vision-based Driver Assistance Systems: Survey, Taxonomy and Advances](https://arxiv.org/abs/2104.12583)
[2021-04-27]
基于视觉的驾驶辅助系统：调查、分类学和进展
414 | * [MmWave Radar and Vision Fusion based Object Detection for Autonomous Driving: A Survey](https://arxiv.org/abs/2108.03004)
[2021-08-09]
本篇综述中详细介绍了在自动驾驶任务中的基于毫米波雷达和视觉融合的障碍物检测方法。
415 | * 3D目标检测
416 | * [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://arxiv.org/abs/2106.12735)
[2021-06-25]
多模态三维目标检测在自动驾驶应用中的调研。
417 | * 驾驶状态监控
418 | * [Survey and synthesis of state of the art in driver monitoring](https://arxiv.org/abs/2110.00472)
[2021-10-04]
本篇综述是对驾驶状态监控技术得现状得全面调研。
419 |
420 |
421 |
422 | ## 4.Video视频相关技术（摘要理解/字幕）
423 |
424 | * Video Summarization 视频摘要
425 | * [Video Summarization Using Deep Neural Networks: A Survey](https://arxiv.org/abs/2101.06072)
[2021-01-18]
426 | * 视频字幕
427 | * [Bridging Vision and Language from the Video-to-Text Perspective: A Comprehensive Review](https://arxiv.org/abs/2103.14785)
[2021-03-30]
本篇综述作者对 VTT 问题的最重要方法进行了分类和分析。回顾了常用于训练和测试模型的流行基准数据集和最相关的比赛。对优化过程中使用的自动评估指标和损失函数进行了回顾与比较。以及对每一个主要数据集的最先进结果进行了总结和分析。
428 | * 视频检索
429 | [A Survey on Natural Language Video Localization](https://arxiv.org/abs/2104.00234)
[2021-04-02]
本篇综述是对 Natural Language Video Localization（NLVL）算法的全面调研，先是提出 NLVL 的 pipeline，并分为有监督和弱监督的方法，接着对每种方法的优缺点进行分析。随后，提出对数据集、评估协议和一般性能分析。最后，通过对现有方法的总结，得到一些可能的观点。
430 | * 视频监控
431 | * 人员检索
432 | * [Person Retrieval in Surveillance Using Textual Query: A Review](https://arxiv.org/abs/2105.02414)
[2021-05-07]
使用文本查询进行监控中的人员检索调研
433 | * 异常检测
434 | * [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://arxiv.org/abs/2107.02778)
[2021-07-07]
本篇综述对近十年中用于为检测智能视频监控中的异常情况而开发的各种方法进行了全面调研。
435 | * 行人属性识别
436 | * [Pedestrian attribute recognition: A survey](https://www.sciencedirect.com/science/article/abs/pii/S0031320321004015)
[2021-07-31]
:star:[code](https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List):house:[project](https://sites.google.com/view/ahu-pedestrianattributes/)
是首篇关于行人属性识别（PAR）的调研，对现有的行人属性识别算法做一个简单介绍，并给出PAR的各种研究方向。2021年7月31日已被收录于 Pattern Recognition 期刊。
437 | * 视频分析
438 | * [A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems](https://arxiv.org/abs/2105.14195)
[2021-06-01]
对于以往的文献综述主要在特定应用的视频分析技术，以提高结果的准确性；在本篇综述论文中，作者专注于优化基于神经网络的视频分析系统的性能技术进行了调研。
439 | * 视频预测理解
440 | * [Review of Video Predictive Understanding: Early ActionRecognition and Future Action Prediction](https://arxiv.org/abs/2107.05140)
[2021-07-13]
对各种早期动作识别和未来动作预测算法进行了全面调研
441 | * 视频理解
442 | * [A Survey on Temporal Sentence Grounding in Videos](https://arxiv.org/abs/2109.08039)
[2021-09-17]
本篇综述对 TSGV 任务进行了全面的调研。
443 |
444 |
445 |
446 | ## 3.Visual Question Answering(视觉问答)
447 | * 视频问答
448 | * [Recent Advances in Video Question Answering: A Review of Datasets and Methods](https://arxiv.org/abs/2101.05954)
[2021-01-18]
449 | * [A survey on VQA_Datasets and Approaches](https://arxiv.org/abs/2105.00421)
[2021-05-04]
本篇综述是对2018年以后的 VQA 数据集、指标和模型，尤其是2018年以后的作品的调研
450 | * [Language bias in Visual Question Answering: A Survey and Taxonomy](https://arxiv.org/abs/2111.08531)
[2021-11-17]
视觉问答里的语言偏差综述
451 | * 场景解析
452 | * 场景图
453 | * [Scene Graphs: A Survey of Generations and Applications](https://arxiv.org/abs/2104.01111)
[2021-04-05]
本篇综述是对目前的场景图研究进行了全面的调查。具体来说，首先总结了场景图的一般定义，然后对场景图(SGG)的生成方法进行了全面系统的讨论，并借助先验知识对SGG进行了研究。然后，研究了场景图的主要应用，并总结了最常用的数据集。最后，对场景图的未来发展提出了一些见解。并相信这将是未来场景图研究的一个非常有益的基础。
454 |
455 |
456 |
457 | ## 2.Medical Image(医学影像)
458 |
459 | - [Diagnostic Captioning: A Survey](https://arxiv.org/abs/2101.07299)
[2021-01-20]
医学诊断字幕综述
460 | - [Applications of Deep Learning in Fundus Images: A Review](https://arxiv.org/abs/2101.09864)
[2021-01-26]
[[github](https://github.com/nkicsl/Fundus_Review)]
深度学习在眼底图像中应用综述
461 | - [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://arxiv.org/abs/2101.10599)
[2021-01-27]
医学图像分析综述
462 | - [Domain Adaptation for Medical Image Analysis: A Survey](https://arxiv.org/abs/2102.09508)
[2021-02-19]
旨在调查医学图像分析中域适应方法的最新进展
463 | - [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://arxiv.org/abs/2102.10553)
[2021-02-23]
对基于机器学习的 WSI 分割、分类和检测的综述
464 | - [Deep reinforcement learning in medical imaging: A literature review](https://arxiv.org/abs/2103.05115)
[2021-03-10]
医学影像中DRL的文献综述
465 | - [Deep Learning for Chest X-ray Analysis: A Survey](https://arxiv.org/abs/2103.08700)
[2021-03-17]
本篇综述是对所有在胸部 X 光片上使用深度学习研究的全面调查，包含 2015 年至2021 年所发表的 295 篇论文文献，按照任务对作品进行分类：图像级预测（分类和回归）、分割、定位、图像生成和域适应。在调查中发现有 209 篇论文文献在研究中使用了一个或多个公共数据集。因此作者还提供了一个全面的公共数据集列表，包括图像和标签的数量和类型，以及关于这些数据集各个方面的一些讨论和注意事项。并详细介绍了商业化的应用，以及对目前的技术水平和未来的潜在方向进行了全面的讨论。
466 | * [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://arxiv.org/abs/2103.13588)
[2021-03-26]
本文对医学影像中基于 AI 的 tumor subregion 分析进行调研。
467 | * [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://arxiv.org/abs/2107.00272)
[2021-07-02]
本篇综述给出基于图的深度学习 conceptual grounding，并讨论其目前在肿瘤定位和分类、肿瘤侵袭和分期、图像检索和生存预测方面的成功。
468 | * [A Survey of Applications of Artificial Intelligence for Myocardial Infarction Disease Diagnosis](https://arxiv.org/abs/2107.06179)
[2021-07-14]
本篇综述是基于人工智能、深度学习、机器学习方法对心电图信号 MID 诊断的全面调研。
469 | * [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://arxiv.org/abs/2107.09602)
[2021-07-21]
本篇综述对有关管理 COVID-19 大流行病的最新努力进行了概述，以及 DL 在医学成像方面的几个实施方案的描述。
470 | * [A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions](https://arxiv.org/abs/2107.09543)
[2021-07-21]
总共分析和讨论了 163 篇在癌症成像方面应用对抗性训练技术的论文，并阐述了其方法、优势和局限性。
471 | * 医学图像检测
472 | * [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://arxiv.org/abs/2105.04881)
[2021-05-12]
本篇综述讨论了使用 DL 技术和 MRI 神经成像模式进行的 MS 自动诊断方法的完整调研。同时，对每项工作进行了彻底的回顾和讨论。最后，详细介绍了使用 DL 技术结合 MRI 模式进行 MS 自动诊断的最重要的挑战和未来的方向。
473 | * 医学图像分割
474 | * [A survey on shape-constraint deep learning for medical image segmentation](https://arxiv.org/abs/2101.07721)
[2021-01-20]
医学图像分割综述
475 | * [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://arxiv.org/abs/2103.00429)
[2021-03-02]
476 | * [Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations](https://arxiv.org/abs/2103.06384)
[2021-03-12]
本篇综述是对2014年至2020年期间所发表的主要研究的调查，包含研究人员用于分割肝脏、肝脏肿瘤和肝脏-血管结构的不同机器学习算法。
477 | * [Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives](https://arxiv.org/abs/2106.15707)
[2021-07-01]
本篇综述对利用不同方式进行准确的 cardiac fibrosis（心脏纤维化）和 scar segmentation（疤痕分割）的传统和当前最先进的方法进行了调研。
478 | * [Modality specific U-Net variants for biomedical image segmentation: A survey](https://arxiv.org/abs/2107.04537)
[2021-07-12]用于生物医学图像分割的特定模式 U-Net 变体
479 | * [Medical Image Segmentation using 3D Convolutional Neural Networks: A Review](https://arxiv.org/abs/2108.08467)
[2021-08-20]使用三维卷积神经网络进行医学图像分割综述调研
480 | * 病理图像（WSI）分析
481 | * [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://arxiv.org/abs/2104.06243)
[2021-04-14]
从流行的 CNN 到具有强大潜力的 Transformers，对用于 Whole-slide Image（病理图像） 分析的人工智能神经网络（ANN）调查。
482 | * 牙齿矫正
483 | * [Convolutional Neural Networks in Orthodontics: a review](https://arxiv.org/abs/2104.08886)
[2021-04-20]
卷积神经网络在矫正牙齿中应用调研
484 | * 糖尿病视网膜病变检测
485 | * [A systematic review of transfer learning based approaches for diabetic retinopathy detection](https://arxiv.org/abs/2105.13793)
[2021-05-31]
本片综述重点关注基于 DNN 和迁移学习的 DR 检测应用，对 2015 年至 2020 年间的 38 篇出版物进行了调研。其中已发表的论文用 9 个图和 10 个表进行了总结，给出了关于 22 个预训练的 CNN 模型、12 个 DR 数据集和标准性能指标的信息。
486 | * [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://arxiv.org/abs/2107.00115)
[2021-07-02]
本篇综述涵盖了五年内（2016-2021年）在公开文献中发表的涉及 DR 的 AI 方法文献。此外，还报告一份可用的 DR 数据集的综合清单。共总结了 114 篇符合审查范围的已发表文章，以及列出 43 个主要数据集的清单。
487 | * X射线诊断
488 | * [Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey](https://arxiv.org/abs/2106.00997)
[2021-06-03]
489 | * 异常检测
490 | * [Anomaly Detection in Medical Imaging -- A Mini Review](https://arxiv.org/abs/2108.11986)
[2021-08-30]
本篇综述是对使用医学图像数据进行异常检测的研究调研
491 | * 医学图像分析
492 | * [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://arxiv.org/abs/2109.08685)
[2021-09-21]
493 | * [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://arxiv.org/abs/2111.02398)
[2021-11-05]
494 | * 癌症诊断
495 | * [A survey on deep learning approaches for breast cancer diagnosis](https://arxiv.org/abs/2109.08853)
[2021-09-21]
496 | * AF
497 | * [Atrial Fibrillation: A Medical and Technological Review](https://arxiv.org/abs/2109.08974)
[2021-09-21]
498 | * MRI重建
499 | * [A review of deep learning methods for MRI reconstruction](https://arxiv.org/abs/2109.08618)
[2021-09-20]
500 | * VQA
501 | * [Medical Visual Question Answering: A Survey](https://arxiv.org/abs/2111.10056)
[2021-11-22]
本次调研任务是医学人工智能和流行的 VQA 任务的结合，即对医学视觉问答任务的全面回顾。
502 |
503 |
504 |
505 | ## 1.Unkown(未分)
506 |
507 | - [Urban land-use analysis using proximate sensing imagery: a survey](https://arxiv.org/abs/2101.04827)
[2021-01-14]
对 proximate sensing 支持土地利用分析的最先进方法和公开的数据集进行了全面回顾。
508 | - [Curriculum Learning: A Survey](https://arxiv.org/abs/2101.10382)
[2021-01-27]
机器学习综述
509 | - [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://arxiv.org/abs/2101.11517)
[2021-01-28]
510 | - [A Survey On Universal Adversarial Attack](https://arxiv.org/abs/2103.01498)
[2021-03-03]
511 | - [Material Measurement Units: Foundations Through a Survey](https://arxiv.org/abs/2103.01997)
[2021-03-04]
本篇综述确定一种新兴的计算机视觉支持的物料监测技术，称为物料测量单元(MMU)；对发展 MMU 的相关工作进行了调查；描述了一个部署多个 MMU的物料库存监测传感器网络。
512 | - [Land Cover Mapping in Limited Labels Scenario: A Survey](https://arxiv.org/abs/2103.02429)
[2021-03-04]
IJCAI 2021
本篇文章是对土地覆盖测绘中的挑战和用于解决这些问题的机器学习方法进行了结构化的全面概述。并对该领域推进研究的差距和机会进行了讨论。
513 | - [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://arxiv.org/abs/2103.05113)
[2021-03-10]
本篇综述是对深度神经网络在手术技能评估中的应用的全面调查，包含530篇论文文献。
514 | - [Deep Learning and Machine Vision for Food Processing: A Survey](https://arxiv.org/abs/2103.16106)
[2021-03-31]
本文是对传统的机器学习和深度学习方法，以及可应用于食品加工领域的机器视觉技术进行的调查。总结了近五年来该领域所发表的代表性论文。是根据它们所使用的不同功能和方法来组织的，便于追踪机器视觉系统和图像处理在食品加工领域的最先进方法。
515 | - [Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review](https://arxiv.org/abs/2104.08732)
[2021-04-20]
计算机视觉和机器学习在数字化标本馆中的应用综述
516 | - [A Review on Explainability in Multimodal Deep Neural Nets](https://arxiv.org/abs/2105.07878)
[2021-05-18]
本片综述对多模态深度神经网络的可解释性进行了全面的调查和评论，特别是针对视觉和语言任务。
517 | - [What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review](https://arxiv.org/abs/2105.11277)
[2021-05-25]
得出结论有，许多类型的stains 仍然缺乏高质量的数据集，大多数工作还不够成熟，无法应用于日常的临床诊断。还发现，采用基于深度学习的方法作为首选方法的趋势越来越明显。
518 | - [Recent Advances and Trends in Multimodal Deep Learning: A Review](https://arxiv.org/abs/2105.11087)
[2021-05-25]
多模态深度学习的最新进展和趋势调研
519 | - [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://arxiv.org/abs/2106.03727)
[2021-06-08]
520 | - [A Survey on Bias in Visual Datasets](https://arxiv.org/abs/2107.07919)
[2021-07-19]
视觉数据集中的偏见调查
521 | - [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://arxiv.org/abs/2107.09287)
[2021-07-21]
本篇综述概述了用于数据隐藏的深度学习技术，包括水印和隐写术，以及比较了目前最先进的方法在网络结构和模型性能方面的分类。
522 | * 微生物图像分析
523 | * [Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer](https://arxiv.org/abs/2108.00358)
[2021-08-03]
人工神经网络在微生物图像分析中的应用,从传统的多层感知器到流行的卷积神经网络和潜在的 Visual Transformer 的全面回顾
524 | * MLP
525 | * [Are we ready for a new paradigm shift? A Survey on Visual Deep MLP](https://arxiv.org/abs/2111.04060)
[2021-11-09]
本篇综述旨在全面介绍视觉深度 MLP 模型的最新发展。探究MLP，这个具有全局感受野但没有注意力的最简单结构，会不会成为一个新的计算机视觉范式？
526 |
527 | ## 扫码CV君微信（注明：CV）入微信交流群：
528 |
529 | 
530 |
531 |
532 |
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/2022-CV-Surveys.md:
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1 |
2 |
3 |
[2022-05-12]
52 | * [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://arxiv.org/pdf/2205.10468.pdf)
[2022-05-24]
53 |
54 |
55 |
56 | ## 51.建筑设计
57 | * [Computer vision-based analysis of buildings and built environments: A systematic review of current approaches](https://arxiv.org/abs/2208.00881)
[2022-08-02]
基于计算机视觉的建筑和建筑环境分析综述
58 |
59 |
60 |
61 | ## 50.NLP
62 | * [A Survey of Parameters Associated with the Quality of Benchmarks in NLP](https://arxiv.org/abs/2210.07566)
[2022-10-17]
63 |
64 |
65 |
66 | ## 49.Machine Learning(机器学习)
67 | * [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://arxiv.org/abs/2207.13243)
[2022-07-28]
68 |
69 |
70 |
71 | ## 48.农业
72 | * [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://arxiv.org/abs/2210.11318)
[2022-10-21]
73 |
74 |
75 |
76 | ## 47.工业检测
77 | * [A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing](https://arxiv.org/abs/2206.07481)
[2022-06-16]
本文对用于检测这些缺陷的方法进行了调查或文献回顾，这些方法是基于所使用的不同传感方式，包括光学、放射学、声学和红外热成像。在这项调查中，对所使用的检测方法进行了讨论。传统的和深度学习的方法都被认为是检测芯片连接和电线连接缺陷的方法，同时也考虑了挑战和未来的研究方向。
78 |
79 |
80 |
81 | ## 46.Adversarial attacks(对抗攻击)
82 | * [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://arxiv.org/abs/2206.08304)
[2022-06-17]
83 | * [A Survey on Physical Adversarial Attack in Computer Vision](https://arxiv.org/abs/2209.14262)
[2022-06-29]
84 | * [Physically Adversarial Attacks and Defenses in Computer Vision: A Survey](https://arxiv.org/abs/2211.01671)
[2022-11-04]
85 |
86 |
87 |
88 | ## 45.Data Augmentation(数据增强)
89 | * [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classication Tasks](https://arxiv.org/abs/2206.06544)
[2022-06-15]
本篇综述从图像分类的角度讨论了AutoDA技术出现的根本原因。确定了标准AutoDA模型的三个关键组成部分：搜索空间、搜索算法和评估功能。基于它们的结构，对现有的图像AutoDA方法进行了系统的分类。本文介绍了AutoDA领域的主要工作，讨论了它们的优点和缺点，并提出了几个潜在的未来改进方向。
90 | * [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://arxiv.org/abs/2212.10888)
[2022-12-22]
:star:[code](https://github.com/ChengtaiCao/Awesome-Mix)
91 |
92 |
93 |
94 | ## 44.Fish Habitat Monitoring
95 | * [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://arxiv.org/abs/2206.05394)
[2022-06-14]
96 |
97 |
98 |
99 | ## 43.harmful meme detection
100 | * [Detecting and Understanding Harmful Memes: A Survey](https://arxiv.org/abs/2205.04274)
[2022-05-10]
有害 meme 检测综述
101 |
102 |
103 |
104 | ## 42.Human Analysis
105 | * [Synthetic Data in Human Analysis: A Survey](https://arxiv.org/abs/2208.09191)
[2022-08-22]
106 |
107 |
108 |
109 | ## 41.Clustering(聚类)
110 | * 双聚类算法
111 | * [Biclustering Algorithms Based on Metaheuristics: A Review](https://arxiv.org/abs/2203.16241)
[2022-03-31]
112 |
113 |
114 |
115 | ## 40.Metric Learning(度量学习)
116 | * [Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey](https://arxiv.org/abs/2201.09267)
[2022-01-25]
从spectral（谱系）、概率、深度学习三个方法中对度量学习进行调研回顾。
117 |
118 |
119 |
120 | ## 39.Deepfake Detection(虚假内容检测)
121 | * [A Review of Deep Learning-based Approaches for Deepfake Content Detection](https://arxiv.org/abs/2202.06095)
[2022-02-15]
122 |
123 |
124 |
125 | ## 38.Anomaly Detection(异常检测)
126 | * 工业异常检测
127 | * [A Survey on Unsupervised Industrial Anomaly Detection Algorithms](https://arxiv.org/abs/2204.11161)
[2022-04-26]
128 | * 视觉感官异常检测
129 | * [A Survey of Visual Sensory Anomaly Detection](https://arxiv.org/abs/2202.07006)
[2022-02-16]
:star:[code](https://github.com/M-3LAB/awesome-visual-sensory-anomaly-detection)
首个视觉感官AD的全面调研工作
130 |
131 |
132 |
133 | ## 37.Object Pose Estimation(物体姿势估计)
134 | * 6D
135 | * [Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding](https://arxiv.org/abs/2212.01920)
[2022-12-06]
136 |
137 |
138 |
139 | ## 36.生物特征识别
140 | * 指纹孔隙检测
141 | * [Fingerprint Pore Detection: A Survey](https://arxiv.org/abs/2211.14716)
[2022-11-29]
:star:[code](https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Survey)
142 | * 指纹活性检测
143 | * [Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021](https://arxiv.org/abs/2202.07259)
[2022-02-16]
对2009年至2021年的LivDet版本的指纹演示攻击检测（FPAD）算法的性能评估，并指出它们多年来的演变。
144 | * 手掌静脉识别
145 | * [Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review](https://arxiv.org/abs/2205.10179)
[2022-05-23]
146 | * 手指静脉识别
147 | * [Artificial Neural Networks for Finger Vein Recognition: A Survey](https://arxiv.org/abs/2208.13341)
[2022-08-30]
收集了149篇相关论文,总结基于人工神经网络的指静脉识别的发展。
148 | * 虹膜识别
149 | * [Deep Learning for Iris Recognition: A Survey](https://arxiv.org/abs/2210.05866)
[2022-10-13]
本篇综述是对过去10年中发表的 200 多篇关于虹膜识别深度学习技术最新发展的论文、技术报告和 GitHub 资源库进行的全面回顾，涵盖了算法设计、开源工具、公开挑战和新兴研究等广泛的主题。
150 |
151 |
152 |
153 | ## 35.Semi/Self-Supervised Learning(自/半监督)
154 | * 自监督
155 | * [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://arxiv.org/abs/2206.02353)
[2022-06-07]
156 | * [A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond](https://arxiv.org/abs/2208.00173)
[2022-08-02]
157 | * [A survey on Self Supervised learning approaches for improving Multimodal representation learning](https://arxiv.org/abs/2210.11024)
[2022-10-21]
158 | * [Survey on Self-Supervised Multimodal Representation Learning and Foundation Models](https://arxiv.org/abs/2211.15837)
[2022-11-30]
159 | * 半监督
160 | * [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://arxiv.org/abs/2208.11296)
[2022-08-25]
161 |
162 |
163 |
164 | ## 34.Transfer Learning(迁移学习)
165 | * [A Review of Deep Transfer Learning and Recent Advancements](https://arxiv.org/abs/2201.09679)
[2022-01-25]
166 |
167 |
168 |
169 | ## 33.Reinforcement Learning(强化学习)
170 | * [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://arxiv.org/abs/2206.08016)
[2022-06-17]
171 | * [A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning](https://arxiv.org/abs/2210.04561)
[2022-10-10]
:star:[code](https://github.com/Guozheng-Ma/DA-in-visualRL)
172 |
173 |
174 |
175 | ## 32.Style Transfer(风格迁移)
176 | * [An Overview of Color Transfer and Style Transfer for Images and Videos](https://arxiv.org/abs/2204.13339)
[2022-04-29]
177 |
178 |
179 |
180 | ## 31.Visual Speech(视觉语音)
181 | * [Deep Learning for Visual Speech Analysis: A Survey](https://arxiv.org/abs/2205.10839)
[2022-05-24]
是对基于深度学习的VSA进行的全面回顾。其中专注于两个基本问题：视觉语音识别和视觉语音生成，并对现实的挑战和当前的发展，包括数据集、评估协议、代表方法、SOTA性能、实际问题等进行总结。
182 | * [Learning in Audio-visual Context: A Review, Analysis, and New Perspective](https://arxiv.org/abs/2208.09579)
[2022-08-23]
从不同方面回顾和展望了当前视听学习领域的情况
183 |
184 |
185 |
186 | ## 30.Domain Adaptation(域适应)
187 | * 域适应
188 | * [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://arxiv.org/abs/2208.07422)
[2022-08-17]
深度无监督域适应综述
189 | * [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://arxiv.org/abs/2212.03176)
[2022-12-07]
190 |
191 |
192 |
193 | ## 29.Augmented Reality/Virtual Reality/Robotics(增强/虚拟现实/机器人)
194 | * AR
195 | * [Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces](https://arxiv.org/abs/2203.03254)
[2022-03-08]
对AR增强型人机交互和机器人界面的调查和分类综述，共调研460篇相关文献。
196 | * [Modern Augmented Reality: Applications, Trends, and Future Directions](https://arxiv.org/abs/2202.09450)
[2022-02-24]
197 | * SLAM
198 | * [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://arxiv.org/abs/2209.04497)
[2022-09-13]
:star:[code](https://github.com/MetaSLAM/GPRS)
199 | * [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://arxiv.org/abs/2209.06428)
[2022-09-15]
200 | * [SLAM for Visually Impaired People: A Survey](https://arxiv.org/abs/2212.04745)
[2022-12-12]
201 |
202 |
203 |
204 | ## 28.Capsule networks(胶囊网络)
205 | * [Learning with Capsules: A Survey](https://arxiv.org/abs/2206.02664)
[2022-06-07]
206 |
207 |
208 |
209 | ## 27.Image Synthesis
210 | * [A Survey on 3D-aware Image Synthesis](https://arxiv.org/abs/2210.14267)
[2022-10-27]
:star:[code](https://weihaox.github.io/projects/awesome-3d-aware/)
211 | * 图像合成
212 | * [Human Image Generation: A Comprehensive Survey](https://arxiv.org/abs/2212.08896)
[2022-12-20]
213 |
214 |
215 |
216 | ## 26.Graph Neural Networks(图神经网络)
217 | * [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://arxiv.org/abs/2209.13232)
[2022-09-28]
218 |
219 |
220 |
221 | ## 25.Vision-Language(视觉语言)
222 | * [Debiasing Methods for Fairer Neural Models in Vision and Language ](https://arxiv.org/abs/2211.05617)
[2022-11-11]
223 | * 视觉语言预训练
224 | * [VLP: A Survey on Vision-Language Pre-training](https://arxiv.org/abs/2202.09061)
[2022-02-21]
本篇文章对视觉语言预训练（VLP）的最新进展和新领域进行了调研，包括图像-文本和视频-文本预训练。并表示这是第一份关于VLP的调研。希望它能对VLP领域的未来研究有所启示。
225 | * [A Survey of Vision-Language Pre-Trained Models](https://arxiv.org/abs/2202.10936)
[2022-02-23]
本篇综述对 VL-PTMs 进行了回顾。其中说明了常用的架构，并对它们的优点和缺点进行了讨论。还介绍了几种预训练VL-PTM并使其适应下游任务的主流方法。
226 | * [Vision-and-Language Pretrained Models: A Survey](https://arxiv.org/abs/2204.07356)
[2022-04-18]
227 | * [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://arxiv.org/abs/2210.09263)
[2022-10-18]
228 |
229 |
230 |
231 | ## 24.Attention
232 | * [Visual Attention Methods in Deep Learning: An In-Depth Survey](https://arxiv.org/abs/2204.07756)
[2022-04-19]
本篇综述回顾了 70 多篇与视觉应用中使用的各种注意力机制有关的文章。并对注意力技术以及它们的优点和局限性进行了全面的讨论。
233 | * [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://arxiv.org/abs/2204.12406)
[2022-04-27]
234 |
235 |
236 |
237 | ## 23.OCR
238 | * 手写数字识别
239 | * [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://arxiv.org/abs/2206.02234)
[2022-06-07]
本文分析了孟加拉语手写数字的特点和固有的模糊性，以及对二十年来最先进的数据集和离线BHDR方法的全面了解。
240 | * Logo Detection
241 | * [Deep Learning for Logo Detection: A Survey](https://arxiv.org/abs/2210.04399)
[2022-10-10]
242 | * 表格检测
243 | * [Deep learning for table detection and structure recognition: A survey](https://arxiv.org/abs/2211.08469)
[2022-11-17]
:star:[code](https://github.com/abdoelsayed2016/table-detection-structure-recognition)
244 |
245 |
246 |
247 |
248 | ## 22.Image Segmentation(图像分割)
249 | * [Semantic Segmentation for Thermal Images: A Comparative Survey](https://arxiv.org/abs/2205.13278)
[2022-05-27]
热图像语义分割综述调研
250 | * [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://arxiv.org/abs/2211.08352)
[2022-11-16]
251 | * 细粒度零件分割
252 | * [Parsing Objects at a Finer Granularity: A Survey](https://arxiv.org/abs/2212.13693)
[2022-12-29]
253 |
254 |
255 |
256 | ## 21.Model Compression/Knowledge Distillation/Pruning(模型压缩/知识蒸馏/剪枝)
257 | * [A survey of deep learning optimizers-first and second order methods](https://arxiv.org/abs/2211.15596)
[2022-11-29]
258 | * 剪枝
259 | * [Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey](https://arxiv.org/abs/2205.08099)
[2022-05-18]
通过 Pruning 和 Freezing 深层神经网络的部分内容进行降维训练调研
260 | * ANN
261 | * [A Review on Plastic Artificial Neural Networks: Exploring the Intersection between Neural Architecture Search and Continual Learning](https://arxiv.org/abs/2206.05625)
[2022-06-14]
262 | * KD
263 | * [Teacher-Student Architecture for Knowledge Learning: A Survey](https://arxiv.org/abs/2210.17332)
[2022-11-01]
264 |
265 |
266 |
267 | ## 20.3D
268 | * 深度估计
269 | * [Outdoor Monocular Depth Estimation: A Research Review](https://arxiv.org/abs/2205.01399)
[2022-05-04]
270 | * 表面重建
271 | * [Surface Reconstruction from Point Clouds: A Survey and a Benchmark](https://arxiv.org/abs/2205.02413)
[2022-05-06]
:star:[code](https://gorilla-lab-scut.github.io/SurfaceReconstructionBenchmark/#/introduction)
272 | * 深度补全
273 | * [Deep Depth Completion: A Survey](https://arxiv.org/abs/2205.05335)
[2022-05-12]
274 |
275 |
276 |
277 | ## 19.GAN
278 | * [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://arxiv.org/abs/2204.04707)
[2022-04-12]
GAN在农业中的图像增强作用综述
279 | * [A Comprehensive Survey on Data-Efficient GANs in Image Generation](https://arxiv.org/abs/2204.08329)
[2022-04-19]
对更全面跟系统的 DE-GANs 的调研。
280 | * [Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review](https://arxiv.org/abs/2205.07236)
[2022-05-17]
本篇论文全面研究了 GANs 在解决 COVID-19 数据稀缺和诊断相关挑战方面的作用。
281 | * [Face editing with GAN -- A Review](https://arxiv.org/abs/2207.11227)
[2022-07-25]
GANs 有两个相互竞争的神经网络：一个生成器和一个鉴别器。生成器用于产生新的样本或内容片段，而鉴别器则用于识别内容片段是真实的还是生成的。它与其他生成式模型的不同之处在于它能够学习未标记的样本。本篇文章回顾了 GANs 如何应用于一系列的应用，包括逼真的图像、文本生成，甚至人类姿势的合成。以及对 GANs 的演变、所提出的几项改进以及不同模型之间进行简要比较。
282 |
283 |
284 |
285 | ## 18.NAS(神经架构搜索)
286 | * [SuperNet in Neural Architecture Search: A Taxonomic Survey](https://arxiv.org/abs/2204.03916)
[2022-04-11]
神经架构搜索的 SuperNet 分类法调研
287 |
288 |
289 |
290 |
291 | ## 17.Point Clouds(点云)
292 | * [Unsupervised Representation Learning for Point Clouds: A Survey](https://arxiv.org/abs/2202.13589)
[2022-03-01]
:star:[code](https://github.com/xiaoaoran/3d_url_survey)
本篇综述对使用 DNN 的无监督点云表征学习进行了全面回顾。
293 | * [Sequential Point Clouds: A Survey](https://arxiv.org/abs/2204.09337)
[2022-04-21]
294 | * 点云补全
295 | * [Comprehensive Review of Deep Learning-Based 3D Point Clouds Completion Processing and Analysis](https://arxiv.org/abs/2203.03311)
[2022-03-08]
296 |
297 |
298 |
299 | ## 16.Human Action Recognition and Detection(人体动作识别与检测)
300 | * [Continuous Human Action Recognition for Human-Machine Interaction: A Review](https://arxiv.org/abs/2202.13096)
[2022-03-01]
301 | * [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://arxiv.org/abs/2206.01038)
[2022-06-03]
302 | * 基于视频的动作识别
303 | * [Video-based Human Action Recognition using Deep Learning: A Review](https://arxiv.org/abs/2208.03775)
[2022-08-09]
304 | * 动作质量评估
305 | * [A Survey of Video-based Action Quality Assessment](https://arxiv.org/abs/2204.09271)
[2022-04-21]
对基于视频的动作质量评估的现有论文进行了全面的调查。
306 | * Transformer
307 | * [Vision Transformers for Action Recognition: A Survey](https://arxiv.org/abs/2209.05700)
[2022-09-14]
308 |
309 |
310 |
311 | ## 15.Image Super-resolution(超分辨率)
312 | * [A Review of Deep Learning Based Image Super-resolution Techniques](https://arxiv.org/abs/2201.10521)
[2022-01-26]
313 | * [Infrared Image Super-Resolution: Systematic Review, and Future Trends](https://arxiv.org/abs/2212.12322)
[2022-12-26]
:star:[code](https://github.com/yongsongH/Infrared_Image_SR_Survey)
314 | * 单图像超分辨率
315 | * [Single Image Super-Resolution Methods: A Survey](https://arxiv.org/abs/2202.11763)
[2022-02-25]
316 | * [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://arxiv.org/abs/2204.13620)
[2022-04-29]
317 |
318 |
319 |
320 | ## 14.Auto Driving(自动驾驶)
321 | * [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://arxiv.org/abs/2202.02703)
[2022-02-08]
本篇综述对现有的基于多模态的自主驾驶感知任务的方法进行了文献调研。其中包含 50 多篇利用感知传感器（包括LiDAR和相机）试图解决目标检测和语义分割任务的论文。期望为自主驾驶感知任务提出一个新的多模态融合方法分类法，并引发对未来基于融合技术的思考。
322 | * [Vision-Centric BEV Perception: A Survey](https://arxiv.org/abs/2208.02797)
[2022-08-05]
:star:[code](https://github.com/4DVLab/Vision-Centric-BEV-Perception)
323 | * [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://arxiv.org/abs/2208.10590)
[2022-08-24]
324 | * [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://arxiv.org/abs/2209.05324)
[2022-09-13]
:star:[code](https://github.com/OpenPerceptionX/BEVPerception-Survey-Recipe)
325 | * [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://arxiv.org/abs/2211.05120)
[2022-11-11]
326 | * 三维人体运动预测
327 | * [3D Human Motion Prediction: A Survey](https://arxiv.org/abs/2203.01593)
[2022-03-04]
本篇综述系统地回顾了 2015 年以来人类运动预测领域的所有相关期刊和会议论文，并对相关方法、数据集、基准、性能进行了讨论分析。
328 | * 行为预测
329 | * [Didn't see that coming: a survey on non-verbal social human behavior forecasting](https://arxiv.org/abs/2203.02480)
[2022-03-07]
330 | * 鱼眼相机
331 | * [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://arxiv.org/abs/2205.13281)
[2022-05-27]
用于自动驾驶的环视鱼眼相机感知综述调研
332 | * 地图
333 | * [High-Definition Map Generation Technologies For Autonomous Driving: A Review](https://arxiv.org/abs/2206.05400)
[2022-06-14]
334 | * Visual Map Localization
335 | * [A Survey on Visual Map Localization Using LiDARs and Cameras](https://arxiv.org/abs/2208.03376)
[2022-08-09]
336 | * 智能交通
337 | * [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://arxiv.org/abs/2208.09588)
[2022-08-23]
本文对智能城市的事故检测和自主交通系统中的动作识别进行了深入的回顾。
338 | * 行为意图预测
339 | * [Behavioral Intention Prediction in Driving Scenes: A Survey](https://arxiv.org/abs/2211.00385)
[2022-11-02]
340 |
341 |
342 |
343 | ## 13.Human Pose Estimation(人体姿态估计)
344 | * [A survey of top-down approaches for human pose estimation](https://arxiv.org/abs/2202.02656)
[2022-02-08]
本篇论文的目的是为研究人员提供基于深度学习方法的二维图像的人体姿态估计的广泛回顾，自2016年以来，这些方法只专注于自上而下的方法。
345 | * 3D人体网格结构恢复
346 | * [Recovering 3D Human Mesh from Monocular Images: A Survey](https://arxiv.org/abs/2203.01923)
[2022-03-04]
:star:[code](https://github.com/tinatiansjz/hmr-survey)
本篇报告对过去十年中的三维人体网状结构恢复方法进行了全面的概述,是第一篇专注于单目三维人体网状结构恢复任务的调查报告。
347 | * 2D人体姿态估计
348 | * [2D Human Pose Estimation: A Survey](https://arxiv.org/abs/2204.07370)
[2022-04-18]
对 200 多项研究贡献，从 network architecture design（网络架构设计）、network training refinement（网络训练细化）和 post processing（后处理）三个方向对二维人体姿态估计进行了全面调研。
349 | * 3D手部姿势估计
350 | * [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://arxiv.org/abs/2206.02257)
[2022-06-07]
351 | * 人体坠落/跌倒检测
352 | * [Vision-based Human Fall Detection Systems using Deep Learning: A Review](https://arxiv.org/abs/2207.10952)
[2022-07-25]
人的跌倒是非常关键的健康问题之一，特别是对于独居的老人和残疾人。全世界老年人口的数量正在稳步增加。因此，人体跌倒检测正在成为这些人辅助生活的有效技术。对于辅助生活，深度学习和计算机视觉已被大量使用。在这篇综述中，讨论了基于深度学习（DL）的最先进的非侵入式（基于视觉）跌倒检测技术。提出一项关于跌倒检测基准数据集的调查。为了清楚地了解，简要地讨论了用于评估跌倒检测系统性能的不同指标。另外还给出基于视觉的人类跌倒检测技术的未来方向。
353 |
354 |
355 |
356 | ## 12.Image Processing(图像处理)
357 | * Image Compression(图像压缩)
358 | * [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://arxiv.org/abs/2201.09240)
[2022-01-25]
本篇综述是对过去五年的利用ML架构进行有损图像压缩技术的调研。
359 | * 去模糊
360 | * [Blind Image Deblurring: a Review](https://arxiv.org/abs/2201.10522)
[2022-01-26]
361 | * [Deep Image Deblurring: A Survey](https://arxiv.org/abs/2201.10700)
[2022-01-27]
362 | * [A Survey on Image Deblurring](https://arxiv.org/abs/2202.07456)
[2022-02-16]
本篇图像去模糊综述分别对传统的图像去模糊方法和深度表示的图像去模糊方法进行了全面调研。
363 | * 图像修复
364 | * [All One Needs to Know about Priors for Deep Image Restoration and Enhancement: A Survey](https://arxiv.org/abs/2206.02070)
[2022-06-07]
:star:[code](https://github.com/yunfanLu/Awesome-Image-Prior)
365 | * 图像增强
366 | * [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://arxiv.org/abs/2205.01491)
[2022-05-04]
367 | * Hyperspectral Unmixing
368 | * [Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review](https://arxiv.org/abs/2205.09933)
[2022-05-23]
本篇文章作者对为高光谱解混提出的基于NMF的方法进行了全面调研。
369 | * [Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond](https://arxiv.org/abs/2212.10772)
[2022-12-22]
370 | * Fake Detection
371 | * [A Survey of Deep Fake Detection for Trial Courts](https://arxiv.org/abs/2205.15792)
[2022-06-01]
372 | * 高分辨率图像视频处理
373 | * [Efficient High-Resolution Deep Learning: A Survey](https://arxiv.org/abs/2207.13050)
[2022-07-26]
374 | * 网格去噪
375 | * [Geometric and Learning-based Mesh Denoising: A Comprehensive Survey](https://arxiv.org/abs/2209.00841)
[2022-09-05]
376 | * 高光谱影像
377 | * [A Guide to Employ Hyperspectral Imaging for Assessing Wheat Quality at Different Stages of Supply Chain in Australia: A Review](https://arxiv.org/abs/2209.05727)
[2022-09-14]
378 |
379 |
380 |
381 | ## 11.Object Tracking(目标跟踪)
382 | * [Single Object Tracking Research: A Survey](https://arxiv.org/abs/2204.11410)
[2022-04-26]
本文介绍了近十年来视频目标跟踪领域两大主流算法框架（基于相关滤波和孪生网络的目标跟踪算法）的基本原理、改进策略和代表性工作，之后按照网络结构分类介绍了其他基于深度学习的目标跟踪算法，还从解决目标跟踪所面临挑战的角度介绍了应对各类问题的典型解决方案，并总结了视频目标跟踪的历史发展脉络和未来发展趋势。
383 | * [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://arxiv.org/abs/2205.04281)
[2022-05-10]
:star:[code](https://github.com/vision4robotics/SiameseTracking4UAV)
本文对领先的 Siamese 跟踪器进行了全面的回顾，同时基于使用典型的无人机机载处理器的评估，对无人机进行了详尽的具体分析。
384 | * RGBT Tracking
385 | * [A Survey for Deep RGBT Tracking](https://arxiv.org/abs/2201.09296)
[2022-01-25]
本篇综述是对近期基于深度神经网络的RGBT追踪器的全面调研，得出MDNet和Siamese架构在RGBT任务中的两个主流框架，前者取得了更高的性能，而后者则满足了实时性要求。以及应对更大数据集应用时，应进一步考虑整合端到端框架，如Siamese和Transformer，以满足实时性以及更强的性能。
386 | * 视觉目标跟踪
387 | * [Visual Object Tracking on Multi-modal RGB-D Videos: A Review](https://arxiv.org/abs/2201.09207)
[2022-01-25]
本篇综述的目的是总结RGB-D跟踪研究中的相关知识。
388 | * [Visual Object Tracking in First Person Vision](https://arxiv.org/abs/2209.13502)
[2022-09-28]
:star:[code](https://machinelearning.uniud.it/datasets/trek150/)
389 | * 多目标跟踪
390 | * [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://arxiv.org/abs/2205.10766)
[2022-05-24]
对多目标跟踪（MOT）中的嵌入方法进行了全面调查和深入分析。
391 | * [Multiple Object Tracking in Recent Times: A Literature Review](https://arxiv.org/abs/2209.04796)
[2022-09-13]
392 |
393 |
394 |
395 | ## 10.Object Detection(目标检测)
396 | * [A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey](https://arxiv.org/abs/2208.10895)
[2022-08-24]
397 | * YOLO
398 | * [Real Time Object Detection System with YOLO and CNN Models: A Review](https://arxiv.org/abs/2208.00773)
[2022-08-02]
使用YOLO和CNN模型的实时目标体检测系统综述
399 | * 3D Object Detection
400 | * [Survey and Systematization of 3D Object Detection Models and Methods](https://arxiv.org/abs/2201.09354)
[2022-01-25]
本篇综述是对过去10年中大量不同的3D目标检测方法的全面调研。
401 | * [3D Object Detection from Images for Autonomous Driving: A Survey](https://arxiv.org/abs/2202.02980)
[2022-02-08]
:star:[code](https://github.com/xinzhuma/3dodi-survey)
本篇论文是第一项调查基于图像的自主驾驶 3D 检测方法的工作。其中包含 80 多个基于图像的 3D 检测方法和从 2015 年到 2021 年间的 200 多个相关研究工作。
402 | * [3D Object Detection for Autonomous Driving: A Survey](https://arxiv.org/abs/2106.10823)
[2022-05-25]
403 | * 结构裂缝检测
404 | * [What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification](https://arxiv.org/abs/2202.03714)
[2022-02-09]
本篇综述旨在让研究人员对裂缝分析算法领域内已发表的利用深度学习的工作有一个概览。
405 | * 坑洞检测
406 | * [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://arxiv.org/abs/2204.13590)
[2022-04-29]
407 | * 持续目标检测
408 | * [Continual Object Detection: A review of definitions, strategies, and challenges](https://arxiv.org/abs/2205.15445)
[2022-06-01]
409 | * 小目标检测
410 | * [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://arxiv.org/abs/2207.14096)
[2022-07-29]
:star:[code](https://github.com/shaunyuan22/SODA)
411 | * 水下目标检测
412 | * [Review On Deep Learning Technique For Underwater Object Detection](https://arxiv.org/abs/2209.10151)
[2022-09-22]
413 | * 牛识别
414 | * [A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions](https://arxiv.org/abs/2210.09215)
[2022-10-18]
415 |
416 |
417 |
418 | ## 9.Video
419 | * 视频摘要
420 | * [Video Summarization Overview](https://arxiv.org/abs/2210.11707)
[2022-10-24]
421 | * 视频理解
422 | * [The Elements of Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://arxiv.org/abs/2201.08071)
[2022-01-21]
423 | * 视频分析
424 | * [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://arxiv.org/abs/2203.02281)
[2022-03-07]
本文对体育视频分析的各种应用进行了全面的回顾，如球员的检测和分类，跟踪体育运动中的球员或球，预测球员或球的轨迹，识别球队的策略，对体育中的各种事件进行分类。
425 | * [Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey](https://arxiv.org/abs/2211.10412)
[2022-11-21]
426 | * [Deep Learning-Driven Edge Video Analytics: A Survey](https://arxiv.org/abs/2211.15751)
[2022-11-30]
427 | * 视频监控
428 | * [Drivers' attention detection: a systematic literature review](https://arxiv.org/abs/2204.03741)
[2022-04-11]
驾驶员注意力检测综述
429 | * [An Overview of Violence Detection Techniques: Current Challenges and Future Directions](https://arxiv.org/abs/2209.11680)
[2022-09-26]
430 | * 视频编码
431 | * [Task Oriented Video Coding: A Survey](https://arxiv.org/abs/2208.07313)
[2022-08-16]
432 | * 时序动作分割
433 | * [Temporal Action Segmentation: An Analysis of Modern Technique](https://arxiv.org/abs/2210.10352)
[2022-10-20]
:star:[code](https://github.com/atlas-eccv22/awesome-temporal-action-segmentation)
434 |
435 |
436 |
437 | ## 8.Transformer
438 | * [Video Transformers: A Survey](https://arxiv.org/abs/2201.05991)
[2022-01-19]
439 | * [Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work](https://arxiv.org/abs/2203.01536)
[2022-03-04]
:star:[code](https://github.com/khawar512/ViT-Survey)
440 | * [Transformers in 3D Point Clouds: A Survey](https://arxiv.org/abs/2205.07417)
[2022-05-17]
旨在全面介绍为各种任务（如点云分类、分割、目标检测等）设计的 3D Transformers。
441 | * [A Comprehensive Survey of Transformers for Computer Vision](https://arxiv.org/abs/2211.06004)
[2022-11-14]
442 | * 多模态学习
443 | * [Multimodal Learning with Transformers: A Survey](https://arxiv.org/abs/2206.06488)
[2022-06-15]
本文对面向多模态数据的Transformer技术进行了全面调查。该调查的主要内容包括 (1)多模态学习、Transformer生态系统和多模态大数据时代的背景，(2)从几何拓扑学的角度对Vanilla Transformer、Vision Transformer和多模态Transformer进行了理论回顾，(3)通过两个重要范式对多模态Transformer的应用进行回顾，即。(4)总结多模态变换器模型和应用的共同挑战和设计，(5)讨论社区的开放问题和潜在研究方向。
444 |
445 |
446 |
447 | ## 7.3D
448 | * [Deep Generative Models on 3D Representations: A Survey](https://arxiv.org/abs/2210.15663)
[2022-10-28]
449 | * 基于 UAV 的三维重建
450 | * [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://arxiv.org/abs/2205.03716)
[2022-05-10]
451 | * 深度估计
452 | * [A Survey on RGB-D Datasets](https://arxiv.org/abs/2201.05761)
[2022-01-19]
453 | * 三维视觉
454 | * [3D Vision with Transformers: A Survey](https://arxiv.org/abs/2208.04309)
[2022-08-09]
:star:[code](https://github.com/lahoud/3d-vision-transformers)
455 |
456 |
457 |
458 | ## 6.Face(人脸)
459 | * [A Survey on Face Recognition Systems](https://arxiv.org/abs/2201.02991)
[2022-01-11]
460 | * [A Survey of Face Recognition](https://arxiv.org/abs/2212.13038)
[2022-12-27]
461 | * 跨光谱人脸识别
462 | * [Beyond the Visible: A Survey on CFace Recognition](https://arxiv.org/abs/2201.04435)
[2022-01-13]
463 | * 口罩人脸检测
464 | * [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://arxiv.org/abs/2201.04777)
[2022-01-14]
465 | * [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://arxiv.org/abs/2211.03705)
[2022-11-08]
466 | * GAN-face检测
467 | * [GAN-generated Faces Detection: A Survey and New Perspectives](https://arxiv.org/abs/2202.07145)
[2022-02-16]
本次综述旨在对 GAN-face 检测的最新进展进行全面回顾。并着重研究能够检测由 GAN 模型生成或合成的人脸图像的方法。
468 | * 眼周生物识别技术
469 | * [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://arxiv.org/abs/2203.15203)
[2022-03-30]
470 | * 人脸属性编辑
471 | * [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://arxiv.org/abs/2205.10587)
[2022-05-24]
使用生成式对抗网络进行人脸属性语义编辑的全面调查
472 | * 人脸修复
473 | * [A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal](https://arxiv.org/abs/2211.02831)
[2022-11-08]
:star:[code](https://github.com/TaoWangzj/Awesome-Face-Restoration)
474 | * 面部情绪识别
475 | * [A survey of smart classroom: Concept, technologies and facial emotions recognition application](https://arxiv.org/abs/2212.01675)
[2022-12-06]
476 | * 篡改检测
477 | * [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://arxiv.org/abs/2212.05667)
[2022-12-13]
478 | * 人脸合成
479 | * [Face Generation and Editing with StyleGAN: A Survey](https://arxiv.org/abs/2212.09102)
[2022-12-20]
480 |
481 |
482 |
483 | ## 5.UAV\Remote Sensing\Satellite Image(无人机\遥感\卫星图像)
484 | * [A Survey on Fundamental Concepts and Practical Challenges of Hyperspectral images](https://arxiv.org/abs/2210.16237)
[2022-10-31]
485 | * 无人机
486 | * [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://arxiv.org/abs/2211.04324)
[2022-11-09]
487 | * 空中监测
488 | * [The State of Aerial Surveillance: A Survey](https://arxiv.org/abs/2201.03080)
[2022-01-11]
从计算机视觉和模式识别的角度对以人为中心的空中监视任务进行全面调研。
489 | * 遥感
490 | * [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://arxiv.org/abs/2205.01380)
[2022-05-04]
基于 DL 的多模态RS数据融合的系统概况
491 | * [Transformers in Remote Sensing: A Survey](https://arxiv.org/abs/2209.01206)
[2022-09-05]
:star:[code](https://github.com/VIROBO-15/Transformer-in-Remote-Sensing)
本篇综述是第一次对遥感中 transformers 的最新进展进行系统的回顾,其中涵盖了60多个最新的基于 transformers 的方法，用于遥感子领域的不同遥感问题：very high-resolution（VHR）、高光谱（HSI）和合成孔径雷达（SAR）图像。
492 | * 张量分解
493 | * [Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review](https://arxiv.org/abs/2205.06407)
[2022-05-16]
遥感中高光谱数据处理的张量分解
494 | * 树皮甲虫攻击检测
495 | * [Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review](https://arxiv.org/abs/2210.03829)
[2022-10-10]
496 |
497 |
498 |
499 | ## 4.Person ReID
500 | * 遮挡行人重识别
501 | * [Deep Learning-based Occluded Person Re-identification: A Survey](https://arxiv.org/abs/2207.14452)
[2022-08-01]
502 | * 步态识别
503 | * [Gait Recognition Based on Deep Learning: A Survey](https://arxiv.org/abs/2201.03323)
[2022-01-11]
本篇综述提供一个关于通过步态识别进行生物识别的最新工作的调查汇编，重点是深度学习方法，强调其优点，并揭露其缺点。此外，它还对数据集、方法和架构进行分类和描述，以解决相关的限制。
504 | * 行人识别
505 | * [A Brief Survey on Person Recognition at a Distance](https://arxiv.org/abs/2212.08969)
[2022-12-20]
506 |
507 |
508 |
509 | ## 3.🏥Medical Image(医学影像)
510 | * [Deep Learning for Computational Cytology: A Survey](https://arxiv.org/abs/2202.05126)
[2022-02-13]
本篇综述调研了 120 多篇基于深度学习的方法在计算细胞学中的进展。
511 | * [Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review](https://arxiv.org/abs/2203.15588)
[2022-03-30]
512 | * [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://arxiv.org/abs/2206.01136)
[2022-06-03]
513 | * [Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential](https://arxiv.org/abs/2206.04425)
[2022-06-10]
514 | * [A Review of Causality for Learning Algorithms in Medical Image Analysis](https://arxiv.org/abs/2206.05498)
[2022-06-14]
515 | * [Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review](https://arxiv.org/abs/2206.05615)
[2022-06-14]
本文对应用于利用胸部X光图像进行COVID-19检测的ML进行了系统回顾，目的是在方法、架构、数据库和目前的局限性方面为研究人员提供一个基线。
516 | * [Texture features in medical image analysis: a survey](https://arxiv.org/abs/2208.02046)
[2022-08-04]
517 | * [3D Brain and Heart Volume Generative Models: A Survey](https://arxiv.org/abs/2210.05952)
[2022-10-13]
:star:[code](https://github.com/csyanbin/3D-Medical-Generative-Survey)
518 | * 自动检测
519 | * [Deep Learning Applications for Lung Cancer Diagnosis: A systematic review](https://arxiv.org/abs/2201.00227)
[2022-01-04]
本篇综述是对深度学习在肺癌自动检测领域应用的全面调研，其中回顾文献是 2016 年至 2021 年该领域的 32 篇会议和期刊文章。
520 | * [AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review](https://arxiv.org/abs/2201.07231)
[2022-01-20]
521 | * [Automated image analysis in large-scale cellular electron microscopy: A literature survey](https://arxiv.org/abs/2206.07171)
[2022-06-16]
本篇综述回顾了当前自动化计算机技术的最先进水平和细胞EM结构分析的主要挑战。讨论了过去五年中为自动生物医学图像分析而开发的先进的计算机视觉、深度学习和软件工具，涉及到EM数据的标注、分割和可扩展性。自动图像采集和分析的整合将允许对具有纳米级分辨率的毫米级数据集进行高通量分析。
522 | * 胎儿生长监测
523 | * [Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images](https://arxiv.org/abs/2201.07935)
[2022-01-21]
本次调研研究了2010年到2021年之间发表的文献，旨在探索人工智能（AI）如何通过超声（US）图像协助胎儿生长监测。
524 | * 组织病理学分析
525 | * [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://arxiv.org/abs/2201.08550)
[2022-01-24]
本篇综述对近年来基于 MV 的图像处理技术在淋巴瘤组织病理学图像中的应用，包括分割、分类和检测进行了全面回顾。
526 | * [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://arxiv.org/abs/2208.08789)
[2022-08-19]
基于高级深度学习的弱监督、半监督和自监督技术在组织病理学图像分析中的综述
527 | * Transformer
528 | * [Transformers in Medical Imaging: A Survey](https://arxiv.org/abs/2201.09873)
[2022-01-25]
:star:[code](https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging)
本篇综述试图对 Transformer 在医学影像中的应用(分割、检测、分类、重建、合成、配准、临床报告生成等)进行全面回顾，涵盖从近期所提出的架构设计到未解决的问题等各个方面。
529 | * [Transformers in Medical Image Analysis: A Review](https://arxiv.org/abs/2202.12165)
[2022-02-25]
本文围绕 Transformer 在不同学习范式中的使用、提高模型效率以及与其他技术的耦合等方面的关键挑战进行了全面研究。
530 | * [Vision Transformers in Medical Imaging: A Review](https://arxiv.org/abs/2211.10043)
[2022-11-21]
531 | * 加密医疗图像
532 | * [A Survey on Patients Privacy Protection with Stganography and Visual Encryption](https://arxiv.org/abs/2201.09388)
[2022-01-25]
533 | * [A Comprehensive Survey on Federated Learning: Concept and Application](https://arxiv.org/abs/2201.09384)
[2022-01-25]
534 | * 跨模态脑图像合成
535 | * [A Survey of Cross-Modality Brain Image Synthesis](https://arxiv.org/abs/2202.06997)
[2022-02-16]
:star:[code](https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis)
首个通过考虑监督水平来深入审查跨模态脑图像合成任务的工作，特别是对于无监督和半监督的跨模态合成。
536 | * 微生物
537 | * [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://arxiv.org/abs/2202.09020)
[2022-02-21]
本篇文章通过 60 多篇文献对基于 DIP 的微生物生物量测量进行全面回顾。
538 | * 医学数据分析
539 | * [Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey](https://arxiv.org/abs/2209.09239)
[2022-09-21]
540 | * 医学图像分析
541 | * [A Survey of Fairness in Medical Image Analysis: Concepts, Algorithms, Evaluations, and Challenges](https://arxiv.org/abs/2209.13177)
[2022-09-28]
542 | * [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://arxiv.org/abs/2210.03736)
[2022-10-10]
543 | * [A Survey on Causal Representation Learning and Future Work for Medical Image Analysis](https://arxiv.org/abs/2210.16034)
[2022-10-31]
544 | * [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://arxiv.org/abs/2211.07804)
[2022-11-16]
:star:[code](https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging)
545 | * 医学图像配准
546 | * [Medical image registration using unsupervised deep neural network: A scoping literature review](https://arxiv.org/abs/2208.01825)
[2022-08-04]
547 | * 医学图像分类
548 | * [Mammograms Classification: A Review](https://arxiv.org/abs/2203.03618)
[2022-03-09]
549 | * 医学图像分割
550 | * [U-Net and its variants for Medical Image Segmentation : A short review](https://arxiv.org/abs/2204.08470)
[2022-04-20]
551 | * [Application of belief functions to medical image segmentation: A review](https://arxiv.org/abs/2205.01733)
[2022-05-05]
对使用信任函数理论的医学图像分割方法进行了回顾。
552 | * [A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images](https://arxiv.org/abs/2205.06486)
[2022-05-16]
对三维和四维医学图像（包括CT、MRI和超声心动图图像）上的自动LAA分割方法进行了回顾。
553 | * [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://arxiv.org/abs/2207.14191)
[2022-07-29]
医学图像分割的深度半监督学习综述
554 | * [Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review](https://arxiv.org/abs/2208.12460)
[2022-08-29]
本篇综述深入分析了过去五年（2017-2022年）发表的126篇基于人工智能的细胞核和腺体实例分割方法的论文，讨论了当前方法的局限性和公开挑战。
555 | * [Deep Learning for Medical Image Segmentation: Tricks, Challenges and Future Directions](https://arxiv.org/abs/2209.10307)
[2022-09-22]
:star:[code](https://github.com/hust-linyi/MedISeg)
556 | * [Medical Image Segmentation Review: The success of U-Net](https://arxiv.org/abs/2211.14830)
[2022-11-29]
:star:[code](https://github.com/NITR098/Awesome-U-Net)
557 | * 皮损分割
558 | * [A Survey on Deep Learning for Skin Lesion Segmentation](https://arxiv.org/abs/2206.00356)
[2022-06-02]
对134篇涉及基于深度学习的皮损分割的研究论文调研
559 | * [Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends](https://arxiv.org/abs/2208.12232)
[2022-08-26]
本篇综述对2011年至2020年间发表的尖端CAD技术进行完整的文献回顾，共包 含365 篇出版物，其中221篇用于皮肤病变分割，144篇用于皮肤病变分类。
560 | * 树状管结构分割
561 | * [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://arxiv.org/abs/2207.11203)
[2022-07-25]
本篇综述总结了文献中人体树状结构分割方法的算法、数据集和评价指标。对不同的算法进行了系统的分类，并以表格的形式报告了感兴趣的解剖区域、使用的数据集和性能指标，帮助研究人员更好地了解可用的选择和方法。通过对文献的分析，基于深度学习的分割方法以其捕捉复杂结构中隐藏信息的优势而成为主流。为此，文章中还提出基于深度学习算法、评价指标和损失函数的可行研究方向，以加速人体树状管结构分割方法的发展和完善。
562 | * 乳腺癌检测
563 | * [Breast cancer detection using artificial intelligence techniques: A systematic literature review](https://arxiv.org/abs/2203.04308)
[2022-03-10]
564 | * 青光眼自动检测
565 | * [Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review](https://arxiv.org/abs/2204.05591)
[2022-04-13]
通过对 28 篇相关论文，对产生和使用分割眼底图像的人工智能青光眼检测框架进行了审查。
566 | * 可解释性
567 | * [Explainable Deep Learning Methods in Medical Diagnosis: A Survey](https://arxiv.org/abs/2205.04766)
[2022-05-11]
568 | * 微循环图像
569 | * [p Learning and Computer Vision Techniques for Microcirculation Analysis: A Revi](https://arxiv.org/abs/2205.05493)
[2022-05-12]
本篇文章对50多篇研究论文进行了调查，并提出最相关和最有前途的计算机视觉算法，以实现微循环图像的自动化分析。
570 | * CT
571 | * [A review of Deep learning Techniques for COVID-19 identification on Chest CT images](https://arxiv.org/abs/2208.00032)
[2022-08-02]
572 | * 糖尿病视网膜病变筛查
573 | * [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://arxiv.org/abs/2208.01810)
[2022-08-04]
574 | * transformer
575 | * [Medical image analysis based on transformer: A Review](https://arxiv.org/abs/2208.06643)
[2022-08-16]
transformer在医学影像分析中的应用综述
576 | * 图像计算
577 | * [Multi-Modality Cardiac Image Computing: A Survey](https://arxiv.org/abs/2208.12881)
[2022-08-30]
本文旨在对心脏病学中的多模态成像、计算方法、验证策略、相关的临床工作流程和未来前景进行全面回顾。
578 | * 微创手术
579 | * [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://arxiv.org/abs/2209.01435)
[2022-09-07]
580 | * 医学增强现实
581 | * [The HoloLens in Medicine: A systematic Review and Taxonomy](https://arxiv.org/abs/2209.03245)
[2022-09-08]
582 | * MRI
583 | * [Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal](https://arxiv.org/abs/2209.09522)
[2022-09-21]
584 | * 阿尔兹海默症自动检测
585 | * [Automated detection of Alzheimer disease using MRI images and deep neural networks- A review](https://arxiv.org/abs/2209.11282)
[2022-09-26]
586 | * 冠心病检测
587 | * [A Review of Modern Approaches for Coronary Angiography Imaging Analysis](https://arxiv.org/abs/2209.13997)
[2022-09-29]
588 | * X-ray
589 | * [Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey](https://arxiv.org/abs/2211.05565)
[2022-11-11]
590 | * 肿瘤生物标志物预测
591 | * [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://arxiv.org/abs/2211.14847)
[2022-11-29]
592 | * 脑年龄估计
593 | * [Deep Learning for Brain Age Estimation: A Systematic Review](https://arxiv.org/abs/2212.03868)
[2022-12-09]
594 |
595 |
596 |
597 | ## 2.Scene Graph Generation(场景图生成)
598 | * [Scene Graph Generation: A Comprehensive Survey](https://arxiv.org/abs/2201.00443)
[2022-01-04]
本篇综述对深度学习技术在这一领域所带来的最新成果进行了全面的调查。回顾了138项涵盖不同输入模式的代表性工作，并从特征提取和融合的角度系统地总结了现有的基于图像的SGG方法。
599 |
600 |
601 |
602 | ## 1.Unkown(未分)
603 | * [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://arxiv.org/abs/2209.06399)
[2022-09-15]
604 | * [Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review](https://arxiv.org/abs/2201.09130)
[2022-01-25]
本篇综述对通过视听特征分析研究自杀意念和自杀行为检测的工作进行了全面调研回顾，主要是自杀性声音/语音声学特征分析和自杀性视觉线索。
605 | * [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://arxiv.org/abs/2202.10284)
[2022-02-22]
606 | * [A systematic review and meta-analysis of Digital Elevation Model (DEM) fusion: pre-processing, methods and applications](https://arxiv.org/abs/2203.15026)
607 | * [A Review of Mobile Mapping Systems: From Sensors to Applications](https://arxiv.org/ftp/arxiv/papers/2205/2205.15865.pdf)
[2022-06-01]
608 | * [Inconsistencies in Measuring Student Engagement in Virtual Learning -- A Critical Review](https://arxiv.org/abs/2208.04548)
[2022-08-10]
609 | * [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://arxiv.org/abs/2209.00796)
[2022-09-09]
610 | * [Data-Free Knowledge Transfer: A Survey](https://arxiv.org/abs/2112.15278)
[2022-01-03]
本篇综述对 Data-Free 知识迁移进行了全面和结构化的调研。
611 | * [Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey](https://arxiv.org/abs/2204.08226)
[2022-04-19]
612 | * [Image Data Augmentation for Deep Learning: A Survey](https://arxiv.org/abs/2204.08610)
[2022-04-20]
613 | * [The Neural Process Family: Survey, Applications and Perspectives](https://arxiv.org/abs/2209.00517)
[2022-09-02]
:star:[code](https://github.com/srvCodes/neural-processes-survey)
614 | * [Handcrafted Feature Selection Techniques for Pattern Recognition: A Survey](https://arxiv.org/abs/2209.02746)
[2022-09-08]
615 | * [Diffusion Models in Vision: A Survey](https://arxiv.org/abs/2209.04747)
[2022-09-13]
616 | * [A Survey of Data Optimization for Problems in Computer Vision Datasets](https://arxiv.org/abs/2210.11717)
[2022-10-24]
:star:[code](https://github.com/Vivian-wzj/DataOptimization-CV)
617 | * [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://arxiv.org/abs/2210.15889)
[2022-10-31]
618 | * [On the Robustness of Explanations of Deep Neural Network Models: A Survey](https://arxiv.org/abs/2211.04780)
[2022-11-10]
619 | * [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://arxiv.org/abs/2211.08064)
[2022-11-16]
620 | * [A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models](https://arxiv.org/abs/2211.09727)
[2022-11-18]
621 | * [A review of laser scanning for geological and geotechnical applications in underground mining](https://arxiv.org/abs/2211.11181)
[2022-11-22]
622 | * [Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey](https://arxiv.org/abs/2211.14709)
[2022-11-29]
623 | * [Attribution-based XAI Methods in Computer Vision: A Review](https://arxiv.org/abs/2211.14736)
[2022-11-29]
624 | * [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://arxiv.org/abs/2212.03954)
[2022-12-09]
625 | * [Deep Learning Methods for Calibrated Photometric Stereo and Beyond: A Survey](https://arxiv.org/abs/2212.08414)
[2022-12-19]
626 | * [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597)
[2022-12-20]
:star:[code](https://github.com/zjunlp/Prompt4ReasoningPapers)
627 | * [A Survey of Deep Learning for Mathematical Reasoning](https://arxiv.org/abs/2212.10535)
[2022-12-21]
:star:[code](https://github.com/lupantech/dl4math)
628 | * 城市规划
629 | * [Visual and Object Geo-localization: A Comprehensive Survey](https://arxiv.org/abs/2112.15202)
[2022-01-03]
本篇综述对涉及图像的地理定位进行了全面的调查，其中包括确定图像的拍摄地点（图像地理定位）或图像中物体的地理定位（物体地理定位）。
630 | * 正则化
631 | * [Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks](https://arxiv.org/abs/2201.03299)
[2022-01-11]
本次调研工作就对过去几年开发的几种正则化方法进行分析，并将调研文献分为“input regularization”、“internal regularization”、“label regularization”三类，且相关文献不超过五年以及所有文献代码都可以在公共资源库中找到。
632 | * 可视化
633 | * [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://arxiv.org/abs/2204.06504)
[2022-04-14]
本篇文章是关于 DL4SciVis 的最先进的调研，涵盖了自 2017 年以来沿着六个维度发表的 59 篇论文，对其相似性和差异性进行了深入讨论，确定其趋势和差距，并概述了研究机会和公开挑战。
634 | * 扩散模型
635 | * [Efficient Diffusion Models for Vision: A Survey](https://arxiv.org/abs/2210.09292)
[2022-10-18]
636 |
637 | ## 扫码CV君微信（注明：CV）入微信交流群：
638 |
639 | 
640 |
641 |
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/2023-CV-Surveys.md:
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1 |
2 |
3 |
32 | 2月份36篇。
33 | 3月份27篇。
34 | 4月份31篇。
35 | 5月份42篇。
36 | 6月份31篇。
37 | 7月份36篇。
38 | 8月份37篇。
39 | 9月份23篇。
40 | 10月份34篇。
41 | 11月份32篇。
计349篇。
42 |
43 | ## 目录
44 |
45 | |:cat:|:dog:|:tiger:|:wolf:|
46 | |------|------|------|------|
47 | |[1.Unkown(未分)](#1)|[2.Human Pose Estimation(人体姿态估计)](#2)|[3.Domain Adaptation(域适应)](#3)|[4.Video(视频相关)](#4)|
48 | |[5.Image Processing(图像处理)](#6)|[6.Image Classification(图像分类)](#6)|[7.Medical Image Processing(医学影像处理)](#7)|[8.Face(人脸)](#8)|
49 | |[9.GAN(生成对抗网络)](#9)|[10.HAR(人体动作识别)](#10)|[11.三维视觉&三维重建](#11)|[12.Object Detection(目标检测)](#12)|
50 | |[13.Image segmentation(图像分割)](#13)|[14.Image Retrieval(图像检索)](#14)|[15.Image Captioning(图像字幕)](#15)|[16.Super-resolution(超分辨率)](#16)|
51 | |[17.Remote Sensing(遥感)](#17)|[18.Object Tracking(目标跟踪)](#18)|[19.VQA(视觉问答)](#19)|
52 |
53 | * 网络犯罪预测
54 | * [Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques](https://arxiv.org/abs/2304.04819)
[2023-04-12]
55 | * 事件信息
56 | * [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://arxiv.org/abs/2302.08890)
[2023-02-20]
57 |
58 | * 内容审核
59 | * [State-of-the-Art in Nudity Classification: A Comparative Analysis](https://arxiv.org/abs/2312.16338)
:star:[code](https://github.com/fcakyon/content-moderation-deep-learning)
[2023-12-29]
60 |
61 | ## Intelligent Agriculture
62 | * 茶叶病害检测
63 | * [Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review](https://arxiv.org/abs/2311.03240)
[2023-11-07]
64 | * 果农叶片病害检测
65 | * [Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review](https://arxiv.org/abs/2311.15741)
[2023-11-28]
66 | * 甜橙叶病
67 | * [A Comprehensive Literature Review on Sweet Orange Leaf Diseases](https://arxiv.org/abs/2312.01756)
[2023-12-05]
68 |
69 | ## Graph Learning(图学习)
70 | * [Graph learning in robotics: a survey](https://arxiv.org/abs/2310.04294)
[2023-10-09]
71 |
72 | ## Multi-view Clustering
73 | * [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://arxiv.org/abs/2309.09473)
[2023-09-19]
74 |
75 | ## Scene Understanding
76 | * [Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation](https://arxiv.org/abs/2310.15676)
[2023-10-25]
77 |
78 | ## Open Set Recognition(开集识别)
79 | * [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://arxiv.org/abs/2312.08785)
[2023-12-15]
80 | * [A Survey on Open-Set Image Recognition](https://arxiv.org/abs/2312.15571)
[2023-12-27]
81 |
82 | ## Emotion Understanding
83 | * [Unlocking the Emotional World of Visual Media:An Overview of the Science, Research, and Impact of Understanding Emotion](https://arxiv.org/abs/2307.13463)
[2023-07-26]
84 |
85 | ## Gaze Estimation(凝视估计)
86 | * [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://arxiv.org/abs/2307.00122)
[2023-07-04]
87 | * [A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding](https://arxiv.org/abs/2307.01470)
[2023-07-06]
88 |
89 | ## Reid(人员重识别/步态识别/行人检测)
90 | * 行人检测
91 | * [Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges](https://arxiv.org/abs/2311.08557)
[2023-11-16]
92 |
93 | ## Human Motion Prediction(人体运动预测)
94 | * [Recent Advances in Deterministic Human Motion Prediction: A Review](https://arxiv.org/abs/2312.06184)
[2023-12-12]
95 |
96 | ## Human Motion Generation(人体动作生成)
97 | * [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://arxiv.org/abs/2308.01618)
[2023-08-04]
98 | * [Human Motion Generation: A Survey](https://arxiv.org/abs/2307.10894)
[2023-07-21]
99 | * [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://arxiv.org/abs/2310.13039)
[2023-10-23]
100 | * 群组识别
101 | * [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://arxiv.org/abs/2307.13541)
[2023-07-26]
102 | * 动作预测
103 | * [A Survey on Deep Learning Techniques for Action Anticipation](https://arxiv.org/abs/2309.17257)
[2023-10-06]
104 | * 牲畜行为识别
105 | * [Application of deep learning for livestock behaviour recognition: A systematic literature review](https://arxiv.org/abs/2310.13483)
[2023-10-23]
106 |
107 | ## Vision-Language(视觉语言)
108 | * [Review of Large Vision Models and Visual Prompt Engineering](https://arxiv.org/abs/2307.00855)
[2023-07-04]
109 | * [Vision Language Transformers: A Survey](https://arxiv.org/abs/2307.03254)
[2023-07-10]
110 | * [A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models](https://arxiv.org/abs/2307.12980)
[2023-07-25]
111 | * [Foundational Models Defining a New Era in Vision: A Survey and Outlook](https://arxiv.org/abs/2307.13721)
[2023-07-27]
:star:[code](https://github.com/awaisrauf/Awesome-CV-Foundational-Models)
112 | * [Vision-Language Instruction Tuning: A Review and Analysis](https://arxiv.org/abs/2311.08172)
[2023-11-15]
:star:[code](https://github.com/palchenli/VL-Instruction-Tuning)
113 | * [Adventures of Trustworthy Vision-Language Models: A Survey](https://arxiv.org/abs/2312.04231)
[2023-12-08]
114 | * LLM
115 | * [A Survey on Multimodal Large Language Models](https://arxiv.org/abs/2306.13549)
[2023-06-26]
:star:[code](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models)
116 | * [Large Language Models Meet Computer Vision: A Brief Survey](https://arxiv.org/abs/2311.16673)
[2023-11-29]
117 |
118 | ## Sound
119 | * [Automated Speaker Independent Visual Speech Recognition: A Comprehensive Survey](https://arxiv.org/abs/2306.08314)
[2023-06-16]
120 |
121 | ## Smart farming(智能农业)
122 | * [Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review](https://arxiv.org/abs/2304.13880)
[2023-04-28]
123 | * [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://arxiv.org/abs/2305.14691)
[2023-05-25]
:star:[code](https://github.com/DongChen06/Label-efficient-in-Agriculture)
124 |
125 | ## Neural Radiance Fields(神经辐射场)
126 | * [Neural Radiance Fields: Past, Present, and Future](https://arxiv.org/pdf/2304.10050.pdf)
[2023-04-21]
127 | * [Neural Radiance Fields (NeRFs): A Review and Some Recent Developments](https://arxiv.org/abs/2305.00375)
[2023-05-02]
128 | * [BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields](https://arxiv.org/abs/2306.03000)
[2023-06-06]
129 | * 渲染
130 | * [Advances in 3D Neural Stylization: A Survey](https://arxiv.org/abs/2311.18328)
[2023-12-01]
131 |
132 | ## 计算成像
133 | * [Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey](https://arxiv.org/abs/2304.08193)
[2023-04-18]
134 | * 相机姿势
135 | * [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://arxiv.org/abs/2305.07348)
[2023-05-15]
136 | * 相机篡改检测
137 | * [A Survey of Feature Types and Their Contributions for Camera Tampering Detection](https://arxiv.org/abs/2310.07886)
[2023-10-13]
138 |
139 | ## OCR
140 | * [A Review On Table Recognition Based On Deep Learning](https://arxiv.org/abs/2312.04808)
141 | * [Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey](https://arxiv.org/abs/2312.11812)
[2023-12-20]
142 |
143 | ## Diffusion Models(扩散模型)
144 | * [A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material](https://arxiv.org/abs/2304.01565)
[2023-04-05]
145 | * [On the Design Fundamentals of Diffusion Models: A Survey](https://arxiv.org/abs/2306.04542)
[2023-06-08]
146 | * [On the Trustworthiness Landscape of State-of-the-art Generative Models: A Comprehensive Survey](https://arxiv.org/abs/2307.16680)
[2023-08-01]
147 |
148 | ## Deep learning(深度学习)
149 | * [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://arxiv.org/abs/2307.09615)
[2023-07-20]
150 | * [A comprehensive review of deep learning in lung cancer](https://arxiv.org/abs/2308.02528)
[2023-08-08]
151 | * 产权保护
152 | * [Turn Passive to Active: A Survey on Active Intellectual Property Protection of Deep Learning Models](https://arxiv.org/abs/2310.09822)
[2023-10-17]
153 |
154 | ## Machine Learning(机器学习)
155 | * [A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals](https://arxiv.org/abs/2310.18795)
[2023-10-31]
156 | * [Defenses in Adversarial Machine Learning: A Survey](https://arxiv.org/abs/2312.08890)
[2023-12-15]
157 | * 迁移学习
158 | * [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://arxiv.org/abs/2306.15110)
[2023-06-28]
159 | * [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://arxiv.org/abs/2309.17192)
[2023-10-02]
160 | * 强化学习
161 | * [Transformers in Reinforcement Learning: A Survey](https://arxiv.org/abs/2307.05979)
[2023-07-13]
162 | * 多任务学习
163 | * [When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review](https://arxiv.org/abs/2307.14382)
[2023-07-28]
164 | * 联邦学习
165 | * [Federated Learning for Computer Vision](https://arxiv.org/abs/2308.13558)
[2023-08-29]
166 | * 度量学习
167 | * [Deep Metric Learning for Computer Vision: A Brief Overview](https://arxiv.org/abs/2312.10046)
[2023-12-19]
168 |
169 | ## Model Compression
170 | * 模型压缩
171 | * [Model Compression Methods for YOLOv5: A Review](https://arxiv.org/abs/2307.11904)
[2023-07-25]
172 | * [Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence](https://arxiv.org/abs/2311.15782)
[2023-11-28]
173 |
174 | ## Sign Language Recognition(手语识别)
175 | * [Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks](https://arxiv.org/abs/2304.14710)
[2023-05-01]
176 |
177 | ## object counting
178 | * [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://arxiv.org/abs/2303.02632)
[2023-03-07]
179 |
180 | ## Data Augmentation(数据增强)
181 | * [Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://arxiv.org/abs/2301.02830)
[2023-01-10]
:star:[code](https://github.com/kmr2017/Advanced-Data-augmentation-codes)
182 | * [Data Distillation: A Survey](https://arxiv.org/abs/2301.04272)
[2023-01-12]
183 | ## Continual Learning(持续学习)
184 | * [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://arxiv.org/abs/2302.00487)
[2023-02-02]
185 | * [A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning](https://arxiv.org/abs/2307.09218)
[2023-07-19]
186 |
187 | ## Adversarial Learning(对抗学习)
188 | * [Physical Adversarial Attacks for Surveillance: A Survey](https://arxiv.org/abs/2305.01074)
[2023-05-03]
189 | * [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://arxiv.org/abs/2305.10862)
[2023-05-19]
190 | * [Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey](https://arxiv.org/abs/2306.06123)
[2023-06-13]
191 | * [A Review of Adversarial Attacks in Computer Vision](https://arxiv.org/abs/2308.07673)
[2023-08-16]
192 | * [A Survey on Transferability of Adversarial Examples across Deep Neural Networks](https://arxiv.org/abs/2310.17626)
[2023-10-27]
193 | * [Adversarial Examples in the Physical World: A Survey](https://arxiv.org/abs/2311.01473)
[2023-11-06]
194 | * 对抗攻击
195 | * [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://arxiv.org/abs/2311.11796)
[2023-11-21]
196 |
197 | ## Incremental Learning(增量学习)
198 | * [Towards Label-Efficient Incremental Learning: A Survey](https://arxiv.org/abs/2302.00353)
[2023-02-02]
:star:[code](https://github.com/kilickaya/label-efficient-il)
199 | * 类增量学习
200 | * [Deep Class-Incremental Learning: A Survey](https://arxiv.org/abs/2302.03648)
[2023-02-08]
:star:[code](https://github.com/zhoudw-zdw/CIL_Survey/)
201 | * [A Survey on Few-Shot Class-Incremental Learning](https://arxiv.org/abs/2304.08130)
[2023-04-18]
202 |
203 | ## Point Clouds(点云)
204 | * [Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey](https://arxiv.org/abs/2305.04691)
[2023-05-09]
205 | * [Self-Supervised Learning for Point Clouds Data: A Survey](https://arxiv.org/abs/2305.11881)
[2023-05-23]
206 | * [3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review](https://arxiv.org/abs/2306.05978)
[2023-06-12]
207 | * [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://arxiv.org/abs/2308.12113)
[2023-08-24]
208 | * 3D点云
209 | * [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://arxiv.org/abs/2305.19812)
[2023-06-01]
:star:[code](https://github.com/xiaoaoran/3D_label_efficient_learning)
210 | * 点云配准
211 | * [Cross-source Point Cloud Registration: Challenges, Progress and Prospects](https://arxiv.org/abs/2305.13570)
[2023-05-24]
212 | * 点云分类
213 | * [Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey](https://arxiv.org/abs/2307.00309)
[2023-07-04]
214 | * [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://arxiv.org/abs/2311.02608)
[2023-11-07]
215 |
216 | ## Image Generation
217 | * [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://arxiv.org/abs/2303.11717)
[2023-03-22]
218 | * Text-to-3D
219 | * [Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era](https://arxiv.org/abs/2305.06131)
[2023-05-11]
220 |
221 | ## Few-Shot Learning
222 | * FSL
223 | * [Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey](https://arxiv.org/abs/2303.08557)
[2023-03-16]
224 | * [A Survey of Deep Visual Cross-Domain Few-Shot Learning](https://arxiv.org/abs/2303.09253)
[2023-03-17]
225 |
226 | ## Self-supervised Learning(自监督)
227 | * [Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training](https://arxiv.org/abs/2305.13689)
[2023-05-24]
228 |
229 | ## Trajectory Prediction
230 | * [Multimodal Trajectory Prediction: A Survey](https://arxiv.org/abs/2302.10463)
[2023-02-22]
231 |
232 | ## Visual Defect Detection(视觉缺陷检测)
233 | * [A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry](https://arxiv.org/abs/2305.13261)
[2023-05-23]
234 | * 路面裂缝检测
235 | * [Deep Learning Approaches in Pavement Distress Identification: A Review](https://arxiv.org/abs/2308.00828)
[2023-08-03]
236 | * 半导体缺陷检测
237 | * [Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review](https://arxiv.org/abs/2308.08376)
[2023-08-17]
238 |
239 | ## Biometric Recognition(生物特征识别)
240 | * [Combining Blockchain and Biometrics: A Survey on Technical Aspects and a First Legal Analysis](https://arxiv.org/abs/2302.10883)
[2023-02-22]
241 | * PAD
242 | * [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://arxiv.org/abs/2305.17522)
[2023-05-30]
243 | * 虹膜识别
244 | * [Deep Learning for Iris Recognition: A Review](https://arxiv.org/abs/2303.08514)
[2023-03-16]
245 | * [Periocular biometrics: databases, algorithms and directions](https://arxiv.org/abs/2307.14111)
[2023-07-27]
246 |
247 |
248 | ## NLP
249 | * [Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media](https://arxiv.org/pdf/2302.08575.pdf)
[2023-02-20]
250 | * [The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges](https://arxiv.org/abs/2303.02411)
[2023-03-07]
251 |
252 | ## SLAM
253 | * [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://arxiv.org/abs/2304.09793)
[2023-04-20]
:star:[code](https://github.com/kun150kun/ESLAM-survey)
254 | * [Deep Learning for Visual Localization and Mapping: A Survey](https://arxiv.org/abs/2308.14039)
[2023-08-29]
255 |
256 | ## Robot
257 | * 机器人
258 | * [A Review of Scene Representations for Robot Manipulators](https://arxiv.org/abs/2301.11275)
[2023-01-27]
259 | * [A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly](https://arxiv.org/abs/2309.13744)
[2023-09-26]
260 | * [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://arxiv.org/abs/2312.08782)
[2023-12-15]
261 | * VPR
262 | * [Visual Place Recognition: A Tutorial](https://arxiv.org/abs/2303.03281)
[2023-03-07]
:star:[code](https://github.com/stschubert/VPR_Tutorial)
263 | * AR/VR
264 | * [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://arxiv.org/abs/2304.09240)
[2023-04-20]
265 | * 虚拟试穿
266 | * [Image-Based Virtual Try-On: A Survey](https://arxiv.org/abs/2311.04811)
[2023-11-09]
:star:[code](https://github.com/little-misfit/Survey-Of-Virtual-Try-On)
267 |
268 | ## Anomaly Detection
269 | * [Deep Industrial Image Anomaly Detection: A Survey](https://arxiv.org/abs/2301.11514)
[2023-01-30]
:star:[code](https://github.com/M-3LAB/awesome-industrial-anomaly-detection)
270 |
271 | ## Domain Adaptation
272 | * DA
273 | * [A Comprehensive Survey on Source-free Domain Adaptation](https://arxiv.org/abs/2302.11803)
[2023-02-24]
274 |
275 | ## KD/Pruning(知识蒸馏)
276 | * 知识蒸馏
277 | * [Knowledge Distillation in Vision Transformers: A Critical Review](https://arxiv.org/ftp/arxiv/papers/2302/2302.02108.pdf)
[2023-02-07]
278 | * [Review helps learn better: Temporal Supervised Knowledge Distillation](https://arxiv.org/abs/2307.00811)
[2023-07-04]
279 | * 剪枝
280 | * [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://arxiv.org/abs/2303.00566)
[2023-03-02]
281 | * [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://arxiv.org/abs/2308.06767)
[2023-08-15]
282 | ## Human-Machine Interaction
283 | * [A Survey on Personalized Affective Computing in Human-Machine Interaction](https://arxiv.org/abs/2304.00377)
[2023-04-04]
284 |
285 | ## Vision-Language
286 | * [Vision-Language Models for Vision Tasks: A Survey](https://arxiv.org/abs/2304.00685)
[2023-04-04]
:star:[code](https://github.com/jingyi0000/VLM_survey)
287 | * [Vision + Language Applications: A Survey](https://arxiv.org/abs/2305.14598)
[2023-05-25]
:star:[code](https://github.com/Yutong-Zhou-cv/Awesome-Text-to-Image)
288 |
289 | ## Autonomous Driving(自动驾驶)
290 | * [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://arxiv.org/abs/2307.04370)
[2023-07-11]
291 | * [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://arxiv.org/abs/2306.09304)
[2023-06-16]
:star:[code](http://autonomous-radars.github.io/)
292 | * [Transformer-Based Sensor Fusion for Autonomous Driving: A Survey](https://arxiv.org/abs/2302.11481)
[2023-02-15]
:star:[code](https://github.com/ApoorvRoboticist/Transformers-Sensor-Fusion)
293 | * [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://arxiv.org/abs/2303.01212)
[2023-03-03]
294 | * [Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review](https://arxiv.org/abs/2308.05731)
[2023-08-11]
295 | * [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://arxiv.org/abs/2304.10410)
[2023-04-21]
:star:[code](https://xjtlu-vec.github.io/Radar-Camera-Fusion)
296 | * [Synthetic Datasets for Autonomous Driving: A Survey](https://arxiv.org/abs/2304.12205)
[2023-04-25]
297 | * [Transformer-based models and hardware acceleration analysis in autonomous driving: A survey](https://arxiv.org/abs/2304.10891)
[2023-04-24]
298 | * [A survey on deep learning approaches for data integration in autonomous driving system](https://arxiv.org/abs/2306.11740)
[2023-06-22]
299 | * [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://arxiv.org/abs/2308.16714)
[2023-09-01]
:star:[code](https://github.com/memberRE/Collaborative-Perception)
300 | * [An Overview about Emerging Technologies of Autonomous Driving](https://arxiv.org/abs/2306.13302)
[2023-06-26]
301 | * [Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions](https://arxiv.org/abs/2311.09093)
[2023-11-16]
302 | * [A Survey on Multimodal Large Language Models for Autonomous Driving](https://arxiv.org/abs/2311.12320)
[2023-11-21]
:star:[code](https://github.com/IrohXu/Awesome-Multimodal-LLM-Autonomous-Driving)
腾讯高精地图/自动驾驶研究团队最新发布的多模态大语言模型自动驾驶综述
303 | * [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://arxiv.org/abs/2312.03408)
[2023-12-07]
:star:[code](https://github.com/OpenDriveLab/DriveAGI)
304 | * [Radar Perception in Autonomous Driving: Exploring Different Data Representations](https://arxiv.org/abs/2312.04861)
[2023-12-11]
:star:[code](https://radar-camera-fusion.github.io/radar)
305 | * 目标检测
306 | * [Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey](https://arxiv.org/abs/2302.06650)
[2023-02-15]
:star:[code](https://github.com/ApoorvRoboticist/VisionBEVDetectionSurvey)
307 | * [Vision-RADAR fusion for Robotics BEV Detections: A Survey](https://arxiv.org/pdf/2302.06643.pdf)
[2023-02-15]
:star:[code](https://github.com/ApoorvRoboticist/Vision-RADAR-Fusion-BEV-Survey)
308 | * 异常检测
309 | * [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://arxiv.org/pdf/2302.02790.pdf)
[2023-02-07]
310 | * 轨迹预测
311 | * [Trajectory-Prediction with Vision: A Survey](https://arxiv.org/abs/2303.13354)
[2023-03-24]
312 | * 驾驶员行为分析
313 | * [Using Visual and Vehicular Sensors for Driver Behavior Analysis: A Survey](https://arxiv.org/abs/2308.13406)
[2023-08-28]
314 | * 交通灯识别
315 | * [Traffic Light Recognition using Convolutional Neural Networks: A Survey](https://arxiv.org/abs/2309.02158)
[2023-09-06]
316 |
317 | ## Neural Architecture Search(神经架构搜索)
318 | * [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://arxiv.org/abs/2304.05405)
[2023-04-13]
319 | * [A Survey on Multi-Objective Neural Architecture Search](https://arxiv.org/abs/2307.09099)
[2023-07-19]
320 |
321 | ## Transformer
322 | * [A Survey on Efficient Training of Transformers](https://arxiv.org/abs/2302.01107)
[2023-02-03]
323 | * [A survey of the Vision Transformers and its CNN-Transformer based Variants](https://arxiv.org/abs/2305.09880)
[2023-05-18]
324 | * [Vision Transformers for Mobile Applications: A Short Survey](https://arxiv.org/abs/2305.19365)
[2023-06-01]
325 | * [A Survey of Techniques for Optimizing Transformer Inference](https://arxiv.org/abs/2307.07982)
[2023-07-18]
326 | * [A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking](https://arxiv.org/abs/2309.02031)
[2023-09-06]
327 | * [Explainability of Vision Transformers: A Comprehensive Review and New Perspectives](https://arxiv.org/abs/2311.06786)
[2023-11-14]
328 |
329 | ## Person Re-Identification
330 | * Reid
331 | * [Deep Learning for Video-based Person Re-Identification: A Survey](https://arxiv.org/abs/2303.11332)
[2023-03-22]
332 | * [A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems](https://arxiv.org/abs/2302.09119)
[2023-02-21]
333 | * [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://arxiv.org/abs/2311.00603)
[2023-11-02]
334 | * 步态识别
335 | * [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://arxiv.org/abs/2309.10144)
[2023-09-20]
336 |
337 |
338 |
339 | ## 19.Visual Question Answering
340 | * [Visual Question Answering: A Survey on Techniques and Common Trends in Recent Literature](https://arxiv.org/abs/2305.11033)
[2023-05-19]
341 |
342 |
343 |
344 | ## 18.Object Tracking(目标跟踪)
345 | * [Transformers in Single Object Tracking: An Experimental Survey](https://arxiv.org/abs/2302.11867)
[2023-02-24]
346 | * 3D目标跟踪
347 | * [3D Multiple Object Tracking on Autonomous Driving: A Literature Review](https://arxiv.org/abs/2309.15411)
[2023-09-28]
348 |
349 |
350 |
351 | ## 17.Remote Sensing(遥感)
352 | * [Automatic detection of aerial survey ground control points based on Yolov5-OBB](https://arxiv.org/abs/2303.03041)
[2023-03-07]
353 | * [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://arxiv.org/abs/2305.05726)
[2023-05-11]
354 | * [Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing -- A Review](https://arxiv.org/abs/2310.08430)
[2023-10-13]
355 | * [A review of individual tree crown detection and delineation from optical remote sensing images](https://arxiv.org/abs/2310.13481)
[2023-10-23]
356 | * 遥感图像检测
357 | * [Oriented Object Detection in Optical Remote Sensing Images: A Survey](https://arxiv.org/abs/2302.10473)
[2023-02-22]
358 | * 变化检测
359 | * [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://arxiv.org/abs/2305.05813)
[2023-05-11]
360 | * [A Survey on Change Detection Techniques in Document Images](https://arxiv.org/abs/2307.07691)
[2023-07-18]
361 | * 检测与跟踪
362 | * [A review of UAV Visual Detection and Tracking Methods](https://arxiv.org/abs/2306.05089)
[2023-06-09]
363 | * 自然火灾检测
364 | * [Wildfire Detection Via Transfer Learning: A Survey](https://arxiv.org/abs/2306.12276)
[2023-06-22]
365 | * 遥感图像分类
366 | * [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://arxiv.org/abs/2306.12111)
[2023-06-22]
367 | * 遥感语义分割
368 | * [Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing](https://arxiv.org/abs/2309.06047)
[2023-09-13]
369 | * 遥感目标检测
370 | * [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://arxiv.org/abs/2309.06751)
[2023-09-14]
371 | * [Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons](https://arxiv.org/abs/2311.07955)
[2023-11-15]
:star:[code](https://github.com/zcj234/MS2ship)
372 |
373 |
374 |
375 | ## 16.Super-resolution(超分辨率)
376 | * [Guided Depth Map Super-resolution: A Survey](https://arxiv.org/abs/2302.09598)
[2023-02-21]
:star:[code](https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey)
377 | * [A Survey on Super Resolution for video Enhancement Using GAN](https://arxiv.org/abs/2312.16471)
[2023-12-29]
378 |
379 |
380 |
381 | ## 15.Video/Image Captioning(视频/图像字幕)
382 | * [Graph Neural Networks in Vision-Language Image Understanding: A Survey](https://arxiv.org/abs/2303.03761)
[2023-03-08]
383 | * VC
384 | * [A Review of Deep Learning for Video Captioning](https://arxiv.org/abs/2304.11431)
[2023-04-25]
385 | * [Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols](https://arxiv.org/abs/2311.02538)
[2023-11-07]
386 |
387 |
388 |
389 | ## 14.Image Retrieval(图像检索)
390 | * [Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques](https://arxiv.org/abs/2312.10089)
[2023-12-19]
391 | * 视频-文本检索
392 | * [Deep Learning for Video-Text Retrieval: a Review](https://arxiv.org/abs/2302.12552)
[2023-02-27]
393 |
394 |
395 |
396 | ## 13.Image segmentation(图像分割)
397 | * [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://arxiv.org/abs/2306.06211)
[2023-06-13]
398 | * [A Comprehensive Review of Modern Object Segmentation Approaches](https://arxiv.org/abs/2301.07499)
[2023-01-19]
399 | * [Semantic Image Segmentation: Two Decades of Research](https://arxiv.org/abs/2302.06378)
[2023-02-15]
400 | * [Transformer-Based Visual Segmentation: A Survey](https://arxiv.org/abs/2304.09854)
[2023-04-20]
:star:[code](https://github.com/lxtGH/Awesome-Segmenation-With-Transformer)
401 | * [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://arxiv.org/abs/2305.08196)
[2023-05-16]
402 | * 人体解析
403 | * [Deep Learning for Human Parsing: A Survey](https://arxiv.org/abs/2301.12416)
[2023-01-31]
404 | * 语义分割
405 | * [A Survey on Semi-Supervised Semantic Segmentation](https://arxiv.org/abs/2302.09899)
[2023-02-21]
406 | * [Semantic Segmentation using Vision Transformers: A survey](https://arxiv.org/abs/2305.03273)
[2023-05-08]
407 | * [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://arxiv.org/abs/2303.04315)
[2023-03-09]
408 | * [Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving](https://arxiv.org/abs/2304.11928)
[2023-04-25]
:star:[code](https://uda-survey.github.io/survey/)
409 | * [A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery](https://arxiv.org/abs/2308.09221)
[2023-08-21]
410 | * 图形抠图
411 | * [Deep Image Matting: A Comprehensive Survey](https://arxiv.org/abs/2304.04672)
[2023-04-11]
:star:[code](https://github.com/JizhiziLi/matting-survey)
412 | * 语义分割
413 | * [Few Shot Semantic Segmentation: a review of methodologies and open challenges](https://arxiv.org/abs/2304.05832)
[2023-04-13]
414 | * [A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application](https://arxiv.org/abs/2310.14277)
[2023-10-24]
:star:[code](https://github.com/YBIO/SurveyCSS)
415 | * 全景分割
416 | * [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://arxiv.org/abs/2304.13980)
[2023-04-28]
417 | * VIS
418 | * [Deep Learning Techniques for Video Instance Segmentation: A Survey](https://arxiv.org/abs/2310.12393)
[2023-10-20]
419 |
420 |
421 |
422 | ## 12.Object Detection(目标检测)
423 | * [Towards Open Vocabulary Learning: A Survey](https://arxiv.org/abs/2306.15880)
[2023-06-29]
:star:[code](https://github.com/jianzongwu/Awesome-Open-Vocabulary)
424 | * [A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond](https://arxiv.org/abs/2304.00501)
[2023-04-04]
425 | * [2D Object Detection with Transformers: A Review](https://arxiv.org/abs/2306.04670)
[2023-06-09]
:star:[code](https://github.com/mindgarage-shan/trans_object_detection_survey)
426 | * 目标定位
427 | * [Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey](https://arxiv.org/abs/2310.12904)
[2023-10-20]
:star:[code](https://github.com/valeoai/Awesome-Unsupervised-Object-Localization)
428 | * 犯罪预测
429 | * [Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions](https://arxiv.org/abs/2303.16310)
[2023-03-30]
430 | * 3D目标检测
431 | * [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://arxiv.org/abs/2311.06043)
[2023-11-13]
432 | * 陨石坑检测
433 | * [Deep Learning based Systems for Crater Detection: A Review](https://arxiv.org/abs/2310.07727)
[2023-10-13]
434 | * 小目标检测
435 | * [Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art](https://arxiv.org/abs/2309.04902)
[2023-09-11]
:star:[code](https://github.com/arekavandi/Transformer-SOD)
436 | * [Small and Dim Target Detection in IR Imagery: A Review](https://arxiv.org/abs/2311.16346)
[2023-11-29]
437 | * 图像线段检测
438 | * [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://arxiv.org/abs/2305.00264)
[2023-05-02]
439 | * 半监督目标检测
440 | * [Semi-supervised Object Detection: A Survey on Recent Research and Progress](https://arxiv.org/abs/2306.14106)
441 | * 开放词汇目标检测
442 | * [A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future](https://arxiv.org/abs/2307.09220)
[2023-07-19]
443 |
444 |
445 |
446 | ## 11.三维视觉&三维重建
447 | * 三维重建
448 | * [3D reconstruction of spherical images: A review of techniques, applications, and prospects](https://arxiv.org/abs/2302.04495)
[2023-02-10]
449 | * [A Review of Deep Learning-Powered Mesh Reconstruction Methods](https://arxiv.org/abs/2303.02879)
[2023-03-07]
450 | * [3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset](https://arxiv.org/abs/2304.09371)
[2023-04-20]
451 | * [Video-Based Rendering Techniques: A Survey](https://arxiv.org/abs/2312.05179)
[2023-12-11]
452 | * 表面重建
453 | * [A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds](https://arxiv.org/abs/2301.13656)
[2023-02-01]
:star:[code](https://github.com/raphaelsulzer/dsr-benchmark)
454 |
455 |
456 |
457 |
458 | ## 10.Human Action Recognition(人体动作识别)
459 | * [A Survey on Human Action Recognition](https://arxiv.org/abs/2301.06082)
[2023-01-18]
460 | * [Transformers in Action Recognition: A Review on Temporal Modeling](https://export.arxiv.org/abs/2302.01921)
[2023-02-06]
461 | * [Deep Neural Networks in Video Human Action Recognition: A Review](https://arxiv.org/abs/2305.15692)
[2023-05-26]
462 | * [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://arxiv.org/abs/2307.03353)
[2023-07-10]
463 |
464 |
465 |
466 | ## 9.Generative Adversarial Network(生成对抗网络)/生成
467 | * [Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification](https://arxiv.org/abs/2301.06080)
[2023-01-18]
468 | * [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://arxiv.org/abs/2302.08641)
[2023-02-20]
469 | * [A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot](https://arxiv.org/abs/2307.14397)
[2023-07-28]
470 | * [Text-to-image Diffusion Model in Generative AI: A Survey](https://arxiv.org/abs/2303.07909)
[2023-03-15]
471 | * [Generative Adversarial Networks for Brain Images Synthesis: A Review](https://arxiv.org/abs/2305.15421)
[2023-05-26]
472 | * [3D GANs and Latent Space: A comprehensive survey](https://arxiv.org/abs/2304.03932)
[2023-04-11]
473 | * [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://arxiv.org/abs/2308.16316)
[2023-09-01]
474 | * 图像编辑
475 | * [Text-guided Image-and-Shape Editing and Generation: A Short Survey](https://arxiv.org/abs/2304.09244)
[2023-04-20]
476 | * 图像合成
477 | * [Survey on Controlable Image Synthesis with Deep Learning](https://arxiv.org/abs/2307.10275)
[2023-07-21]
478 | * [Image Synthesis under Limited Data: A Survey and Taxonomy](https://arxiv.org/abs/2307.16879)
[2023-08-01]
:star:[code](https://github.com/kobeshegu/awesome-few-shot-generation)
479 | * [A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions](https://arxiv.org/abs/2308.13142)
[2023-08-28]
480 | * AIGC
481 | * [AIGC for Various Data Modalities: A Survey](https://arxiv.org/abs/2308.14177)
[2023-08-29]
482 | * 文本-图像生成
483 | * [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://arxiv.org/abs/2309.00810)
[2023-09-06]
484 | * [A Survey of AI Text-to-Image and AI Text-to-Video Generators](https://arxiv.org/abs/2311.06329)
[2023-11-14]
485 |
486 |
487 |
488 |
489 | ## 8.Face(人脸)
490 | * Deepfake检测
491 | * [Deepfake Detection using Biological Features: A Survey](https://arxiv.org/abs/2301.05819)
[2023-01-18]
492 | * 人脸识别
493 | * [Racial Bias within Face Recognition: A Survey](https://arxiv.org/abs/2305.00817)
[2023-05-02]
494 | * 人脸检测
495 | * [A Comparative Study of Face Detection Algorithms for Masked Face Detection](https://arxiv.org/abs/2305.11077)
[2023-05-19]
496 | * 人脸恢复
497 | * [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://arxiv.org/abs/2309.15490)
[2023-09-28]
:star:[code](https://github.com/24wenjie-li/Awesome-Face-Restoration)
498 | * 人脸对齐
499 | * [A survey and classification of face alignment methods based on face models](https://arxiv.org/abs/2311.03082)
[2023-11-07]
500 | * 3D头像
501 | * [Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey](https://arxiv.org/abs/2306.03576)
[2023-06-07]
502 | * 说话头
503 | * [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://arxiv.org/abs/2308.16041)
[2023-08-31]
504 | * 三维人脸重建
505 | * [3D Face Reconstruction: the Road to Forensics](https://arxiv.org/abs/2309.11357)
[2023-09-21]
506 | * 情感识别
507 | * [Emotion Recognition by Video: A review](https://arxiv.org/abs/2310.17212)
[2023-10-27]
508 | * GFVC
509 | * [Generative Face Video Coding Techniques and Standardization Efforts: A Review](https://arxiv.org/abs/2311.02649)
[2023-11-07]
:star:[code](https://github.com/Berlin0610/Awesome-Generative-Face-Video-Coding)
510 |
511 |
512 |
513 | ## 7.Medical Image Processing(医学影像处理)
514 | * [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://arxiv.org/abs/2308.01265)
[2023-08-03]
515 | * [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://arxiv.org/abs/2307.16142)
[2023-08-01]
:star:[code](https://github.com/mindflow-institue/Awesome-Implicit-Neural-Representations-in-Medical-imaging)
516 | * [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://arxiv.org/abs/2307.13125)
[2023-07-26]
517 | * [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://arxiv.org/abs/2307.10246)
[2023-07-21]
518 | * [A scoping review on multimodal deep learning in biomedical images and texts](https://arxiv.org/abs/2307.07362)
[2023-07-17]
519 | * [Diagnostic test accuracy (DTA) of artificial intelligence in digital pathology: a systematic review, meta-analysis and quality assessment](https://arxiv.org/abs/2306.07999)
[2023-06-16]
520 | * [A Survey of Feature detection methods for localisation of plain sections of Axial Brain Magnetic Resonance Imaging](https://arxiv.org/pdf/2302.04173.pdf)
[2023-02-09]
521 | * [Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art](https://arxiv.org/abs/2304.13014)
[2023-04-26]
522 | * [Publicly available datasets of breast histopathology H&E whole-slide images: A systematic review](https://arxiv.org/abs/2306.01546)
[2023-06-05]
523 | * [Computational Pathology: A Survey Review and The Way Forward](https://arxiv.org/abs/2304.05482)
[2023-04-13]
524 | * [Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review](https://arxiv.org/abs/2305.03546)
[2023-05-08]
525 | * [Digitization of Pathology Labs: A Review of Lessons Learned](https://arxiv.org/abs/2306.03619)
[2023-06-07]
526 | * [Federated Learning for Medical Image Analysis: A Survey](https://arxiv.org/abs/2306.05980)
[2023-06-12]
527 | * [Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care](https://arxiv.org/abs/2309.00252)
[2023-09-04]
528 | * [A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images](https://arxiv.org/abs/2309.02555)
[2023-09-07]
529 | * [Systematic Review of Techniques in Brain Image Synthesis using Deep Learning](https://arxiv.org/abs/2309.04511)
[2023-09-11]
530 | * [A Systematic Review of Few-Shot Learning in Medical Imaging](https://arxiv.org/abs/2309.11433)
[2023-09-21]
531 | * [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://arxiv.org/abs/2310.06873)
[2023-10-12]
532 | * [Tracking and Mapping in Medical Computer Vision: A Review](https://arxiv.org/abs/2310.11475)
[2023-10-19]
533 | * [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://arxiv.org/abs/2310.18689)
[2023-10-31]
:star:[code](https://github.com/mindflow-institue/Awesome-Foundation-Models-in-Medical-Imaging)
534 | * [Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade](https://arxiv.org/abs/2311.07609)
[2023-11-15]
535 | * [Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey](https://arxiv.org/abs/2311.10118)
[2023-11-20]
536 | * [CLIP in Medical Imaging: A Comprehensive Survey](https://arxiv.org/abs/2312.07353)
[2023-12-13]
:star:[code](https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging)
537 | * 医学影像分割
538 | * [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://arxiv.org/abs/2303.00232)
[2023-03-02]
539 | * [Attention Mechanisms in Medical Image Segmentation: A Survey](https://arxiv.org/abs/2305.17937)
[2023-05-30]
540 | * [From CNN to Transformer: A Review of Medical Image Segmentation Models](https://arxiv.org/abs/2308.05305)
[2023-08-11]
541 | * [Medical Image Segmentation with Domain Adaptation: A Survey](https://arxiv.org/abs/2311.01702)
[2023-11-06]
542 | * [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://arxiv.org/abs/2311.13964)
[2023-11-27]
543 | * [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://arxiv.org/abs/2312.00634)
[2023-12-04]
544 | * [Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook](https://arxiv.org/abs/2312.05391)
[2023-12-12]
545 | * 医学影像分析
546 | * [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://arxiv.org/abs/2301.03505)
[2023-01-10]
:star:[code](https://github.com/mindflow-institue/Awesome-Transformer)
547 | * [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://arxiv.org/ftp/arxiv/papers/2302/2302.05043.pdf)
[2023-02-13]
548 | * [Is attention all you need in medical image analysis? A review](https://arxiv.org/abs/2307.12775)
[2023-07-25]
549 | * [Data efficient deep learning for medical image analysis: A survey](https://arxiv.org/abs/2310.06557)
[2023-10-11]
550 | * [Domain Generalization for Medical Image Analysis: A Survey](https://arxiv.org/abs/2310.08598)
[2023-10-16]
551 | * [A comprehensive survey on deep active learning and its applications in medical image analysis](https://arxiv.org/abs/2310.14230)
[2023-10-24]
:star:[code](https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis)
552 | * [Continual Learning in Medical Imaging Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://arxiv.org/abs/2312.17004)
[2023-12-29]
553 | * 医学影像配准
554 | * [A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond](https://arxiv.org/abs/2307.15615)
[2023-07-31]
555 | * [Deep learning in medical image registration: introduction and survey](https://arxiv.org/abs/2309.00727)
[2023-09-06]
556 | * 医学影像分类
557 | * [Review of AlexNet for Medical Image Classification](https://arxiv.org/abs/2311.08655)
[2023-11-16]
558 | * 医学报告生成
559 | * [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://arxiv.org/abs/2311.14199)
[2023-11-27]
560 | * 脑微出血检测
561 | * [Review of methods for automatic cerebral microbleeds detection](https://arxiv.org/pdf/2301.13549.pdf)
[2023-02-01]
562 | * 生物医学重建
563 | * [Biomedical Image Reconstruction: A Survey](https://arxiv.org/abs/2301.11813)
[2023-01-30]
564 | * 急性白血病和白细胞的自动检测和分类
565 | * [A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells](https://arxiv.org/abs/2303.03916)
[2023-03-08]
566 | * MRI
567 | * [Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review](https://arxiv.org/abs/2303.09012)
[2023-03-17]
568 | * [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://arxiv.org/abs/2305.06739)
[2023-05-12]
569 | * [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://arxiv.org/abs/2311.11383)
[2023-11-21]
570 | * 青光眼检测
571 | * [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://arxiv.org/abs/2307.16528)
[2023-08-01]
572 | * 细胞分类
573 | * [A Review on Classification of White Blood Cells Using Machine Learning Models](https://arxiv.org/abs/2308.06296)
[2023-08-15]
574 | * 头皮疾病诊断
575 | * [Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review](https://arxiv.org/abs/2308.07052)
[2023-08-15]
576 | * 关节炎诊断
577 | * [Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review](https://arxiv.org/abs/2308.09380)
[2023-08-21]
578 | * 黑色素瘤检测
579 | * [Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review](https://arxiv.org/abs/2309.00265)
[2023-09-04]
580 | * vision transformer
581 | * [Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review](https://arxiv.org/abs/2309.02783)
[2023-09-07]
vision transformer在肺癌诊断中的应用综述
582 | * 脊柱侧弯筛查
583 | * [Intelligent Scoliosis Screening and Diagnosis: A Survey](https://arxiv.org/abs/2310.08756)
[2023-10-16]
584 | * 临床疾病诊断
585 | * [The Significance of Machine Learning in Clinical Disease Diagnosis: A Review](https://arxiv.org/abs/2310.16978)
[2023-10-27]
586 | * 猴痘检测
587 | * [A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection](https://arxiv.org/abs/2311.10754)
[2023-11-21]
588 | * 阿兹海默
589 | * [Multimodal Identification of Alzheimer's Disease: A Review](https://arxiv.org/abs/2311.12842)
[2023-11-23]
590 | * 视网膜
591 | * [A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions](https://arxiv.org/abs/2311.13710)
[2023-10-27]
592 | * 息肉分割
593 | * [A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends](https://arxiv.org/abs/2311.18373)
:star:[code](https://github.com/taozh2017/Awesome-Polyp-Segmentation)
[2023-12-01]
594 | * 癌症检测
595 | * [Survey on deep learning in multimodal medical imaging for cancer detection](https://arxiv.org/abs/2312.01573)
[2023-12-05]
596 | * 肺栓塞诊断
597 | * [Deep learning and traditional-based CAD schemes for the pulmonary embolism diagnosis: A survey](https://arxiv.org/abs/2312.01351)
[2023-12-05]
598 | * 医学VLP
599 | * [Medical Vision Language Pretraining: A survey](https://arxiv.org/abs/2312.06224)
[2023-12-12]
600 | * 乳腺癌诊断
601 | * [Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review](https://arxiv.org/abs/2312.06697)
[2023-12-13]
602 |
603 |
604 |
605 | ## 6.Image Classification(图像分类)
606 | * [A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data](https://arxiv.org/abs/2310.09994)
[2023-10-17]
607 | * [A Comprehensive Study of Vision Transformers in Image Classification Tasks](https://arxiv.org/abs/2312.01232)
[2023-12-05]
608 | * 长尾
609 | * [Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics](https://export.arxiv.org/abs/2302.01507)
[2023-02-06]
610 | * 果实成熟度分类
611 | * [Fruit Ripeness Classification: a Survey](https://arxiv.org/abs/2212.14441)
612 | * 植物病害分类
613 | * [Embracing Limited and Imperfect Data: A Review on Plant Stress Recognition Using Deep Learning](https://arxiv.org/abs/2305.11533)
[2023-05-22]
614 | * [A comprehensive review on Plant Leaf Disease detection using Deep learning](https://arxiv.org/abs/2308.14087)
[2023-08-29]
615 | * [Machine Learning for Leaf Disease Classification: Data, Techniques and Applications](https://arxiv.org/abs/2310.12509)
[2023-10-20]
616 | * 浮游生物识别
617 | * [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://arxiv.org/abs/2305.11739)
[2023-05-22]
618 | * 作物分类
619 | * [A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images](https://arxiv.org/abs/2306.09418)
[2023-06-19]
620 | * 图表分类
621 | * [A Survey and Approach to Chart Classification](https://arxiv.org/abs/2307.04147)
[2023-07-11]
622 | * 图形分类
623 | * [A Survey on Figure Classification Techniques in Scientific Documents](https://arxiv.org/abs/2307.05694)
[2023-07-13]
624 | * 交通信号灯识别
625 | * [Adversarial Attacks on Traffic Sign Recognition: A Survey](https://arxiv.org/abs/2307.08278)
[2023-07-18]
626 |
627 |
628 |
629 | ## 5.Image Processing(图像处理)
630 | * 去雨
631 | * [Towards Unified Deep Image Deraining: A Survey and A New Benchmark](https://arxiv.org/abs/2310.03535)
[2023-10-06]
632 | * 去噪
633 | * [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper ](https://arxiv.org/abs/2301.03362)
[2023-01-10]
634 | * [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://arxiv.org/abs/2308.00247)
[2023-08-02]
635 | * 去模糊
636 | * [A survey on facial image deblurring](https://arxiv.org/pdf/2302.05017.pdf)
[2023-02-13]
637 | * [A Comprehensive Survey on Deep Neural Image Deblurring](https://arxiv.org/abs/2310.04719)
[2023-10-10]
638 | * 去耀斑
639 | * [Toward Flare-Free Images: A Survey](https://arxiv.org/abs/2310.14354)
[2023-10-24]
640 | * 图像恢复
641 | * [Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey](https://arxiv.org/abs/2308.09388)
:star:[code](https://github.com/lixinustc/Awesome-diffusion-model-for-image-processing/)
[2023-08-21]
642 | * 图像增强
643 | * [Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview](https://arxiv.org/abs/2312.00250)
[2023-12-04]
644 | * 质量评估
645 | * [Blind Image Quality Assessment: A Brief Survey](https://arxiv.org/abs/2312.16551)
[2023-12-29]
646 |
647 |
648 |
649 | ## 4.Video(视频相关)
650 | * VAD
651 | * [Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions](https://arxiv.org/abs/2301.00114)
[2023-01-03]
对使用从视频中提取的骨架的隐私保护型深度学习异常检测方法进行了调查
652 | * [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://arxiv.org/pdf/2302.05087.pdf)
[2023-02-13]
653 | * [Survey on video anomaly detection in dynamic scenes with moving cameras](https://arxiv.org/abs/2308.07050)
[2023-08-15]
654 | * 视频错误信息检测
655 | * [Online Misinformation Video Detection: A Survey](https://arxiv.org/abs/2302.03242)
[2023-02-08]
:star:[code](https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection)
656 | * 视频分析
657 | * [Edge-Based Video Analytics: A Survey](https://arxiv.org/abs/2303.14329)
[2023-03-28]
658 | * [A Review of Machine Learning Methods Applied to Video Analysis Systems](https://arxiv.org/abs/2312.05352)
[2023-12-12]
659 | * [Deep Learning Approaches for Seizure Video Analysis: A Review](https://arxiv.org/abs/2312.10930)
[2023-12-19]
660 | * 视频时刻定位
661 | * [A Survey on Video Moment Localization](https://arxiv.org/abs/2306.07515)
[2023-06-14]
662 | * 视频监控
663 | * [Sensors and Systems for Monitoring Mental Fatigue: A systematic review](https://arxiv.org/abs/2307.01666)
[2023-07-06]
664 | * 视频编辑
665 | * [A Survey on Video Diffusion Models](https://arxiv.org/abs/2310.10647)
[2023-10-17]
:star:[code](https://github.com/ChenHsing/Awesome-Video-Diffusion-Models)
666 | * 视频分割
667 | * [Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability](https://arxiv.org/abs/2310.12296)
[2023-10-20]
668 |
669 |
670 |
671 | ## 3.Domain Adaptation/Generalization(域适应/泛化)
672 | * 域适应
673 | * [Source-Free Unsupervised Domain Adaptation: A Survey](https://arxiv.org/abs/2301.00265)
[2023-01-03]
从技术角度对现有的SFUDA方法进行了系统的文献回顾。具体来说，将目前的SFUDA研究分为两类，即白盒SFUDA和黑盒SFUDA，并根据它们使用的不同学习策略进一步划分为更细的子类别。以及研究了每个子类别中方法的挑战，讨论了白盒和黑盒SFUDA方法的优势/劣势，总结了常用的基准数据集，另外还总结了在不使用源数据的情况下提高模型通用性的流行技术。
674 | * 域泛化
675 | * [Domain Generalization in Computational Pathology: Survey and Guidelines](https://arxiv.org/abs/2310.19656)
[2023-10-31]
676 |
677 |
678 |
679 | ## 2.Human Pose Estimation(人体姿态估计)
680 | * [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://arxiv.org/abs/2305.13765)
[2023-05-24]
681 | * [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://arxiv.org/abs/2308.13872)
[2023-08-29]
682 | * [Markerless human pose estimation for biomedical applications: a survey](https://arxiv.org/abs/2308.00519)
[2023-08-02]
683 | * [Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives](https://arxiv.org/abs/2309.12067)
[2023-09-22]
684 | * 人体解析
685 | * [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://arxiv.org/abs/2301.00394)
[2023-01-03]
:star:[code](https://github.com/soeaver/awesome-human-parsing)
686 | * 手势合成
687 | * [A Comprehensive Review of Data-Driven Co-Speech Gesture Generation](https://arxiv.org/abs/2301.05339)
[2023-01-16]
688 | * 动作识别与姿势估计
689 | * [CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey](https://arxiv.org/abs/2301.06187)
[2023-01-18]
690 | * 体育
691 | * [A Survey of Advanced Computer Vision Techniques for Sports](https://arxiv.org/abs/2301.07583)
[2023-01-19]
692 | * 身体语言识别
693 | * [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://arxiv.org/abs/2308.08849)
:star:[code](https://github.com/wentaoL86/awesome-body-language)
[2023-08-21]
694 | * 手势识别
695 | * [Study and Survey on Gesture Recognition Systems](https://arxiv.org/abs/2312.00392)
[2023-12-04]
696 |
697 |
698 |
699 | ## 1.Unkown(未分)
700 | * [Overview of Class Activation Maps for Visualization Explainability](https://arxiv.org/abs/2309.14304)
701 | * [Computation-efficient Deep Learning for Computer Vision: A Survey](https://arxiv.org/abs/2308.13998)
[2023-08-29]
702 | * [A Review on Objective-Driven Artificial Intelligence](https://arxiv.org/abs/2308.10135)
[2023-08-22]
703 | * [Survey on Computer Vision Techniques for Internet-of-Things Devices](https://arxiv.org/abs/2308.02553)
[2023-08-08]
704 | * [Segmentation Framework for Heat Loss Identification in Thermal Images:Empowering Scottish Retrofitting and Thermographic Survey Companies](https://arxiv.org/abs/2308.03631)
[2023-08-08]
705 | * [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://arxiv.org/abs/2307.16235)
[2023-08-01]
706 | * [Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey](https://arxiv.org/abs/2307.16236)
[2023-08-01]
707 | * [Causal reasoning in typical computer vision tasks](https://arxiv.org/abs/2307.13992)
[2023-07-27]
708 | * [Loss Functions and Metrics in Deep Learning. A Review](https://arxiv.org/abs/2307.02694)
[2023-07-07]
709 | * [Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies](https://arxiv.org/abs/2306.07996)
[2023-06-16]
710 | * [Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work](https://arxiv.org/abs/2306.01929)
[2023-06-06]
711 | * [A survey on Organoid Image Analysis Platforms](https://arxiv.org/abs/2301.02341)
[2023-01-09]
712 | * [Physics-Informed Computer Vision: A Review and Perspectives](https://arxiv.org/abs/2305.18035)
[2023-05-30]
713 | * [A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics](https://arxiv.org/abs/2301.13445)
[2023-02-01]
714 | * [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://arxiv.org/pdf/2302.02515.pdf)
[2023-02-07]
715 | * [Deep Learning for Inertial Positioning: A Survey](https://arxiv.org/abs/2303.03757)
[2023-03-08]
716 | * [Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse](https://arxiv.org/abs/2305.00510)
[2023-05-02]
717 | * [Hyperbolic Deep Learning in Computer Vision: A Survey](https://arxiv.org/abs/2305.06611)
[2023-05-12]
718 | * [A Survey of Historical Learning: Learning Models with Learning History](https://arxiv.org/abs/2303.12992)
[2023-03-24]
:star:[code](https://github.com/Martinser/Awesome-Historical-Learning)
719 | * [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://arxiv.org/abs/2303.15361)
[2023-03-28]
:star:[code](https://github.com/tim-learn/awesome-test-time-adaptation)
720 | * [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://arxiv.org/abs/2304.06488)
[2023-04-14]
721 | * [Survey on LiDAR Perception in Adverse Weather Conditions](https://arxiv.org/abs/2304.06312)
[2023-04-14]
722 | * [Hyperbolic Geometry in Computer Vision: A Survey](https://arxiv.org/abs/2304.10764)
[2023-04-24]
723 | * [The Robustness of Computer Vision Models against Common Corruptions: a Survey](https://arxiv.org/abs/2305.06024)
[2023-05-11]
724 | * [A Survey of Methods for Converting Unstructured Data to CSG Models](https://arxiv.org/abs/2305.01220)
[2023-05-03]
725 | * [Latest Trends in Artificial Intelligence Technology: A Scoping Review](https://arxiv.org/abs/2305.04532)
[2023-05-09]
726 | * [Visual Tuning](https://arxiv.org/abs/2305.06061)
[2023-05-11]
727 | * [Non-adversarial Robustness of Deep Learning Methods for Computer Vision](https://arxiv.org/abs/2305.14986)
[2023-05-25]
728 | * [From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks](https://arxiv.org/abs/2310.11884)
[2023-10-19]
729 | * [Trustworthy Large Models in Vision: A Survey](https://arxiv.org/abs/2311.09680)
[2023-11-17]
730 | * [Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts](https://arxiv.org/abs/2312.01540)
[2023-12-05]
731 | * [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://arxiv.org/abs/2312.03014)
[2023-12-07]
732 | * [Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization](https://arxiv.org/abs/2312.14697)
:star:[code](https://github.com/vibot-lab/Pola4all_JEI_2023)
[2023-12-25]
733 | * DNN
734 | * [Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing](https://arxiv.org/abs/2304.04906)
[2023-04-12]
:star:[code](https://github.com/MehediHasanTutul/Reject_option)
735 | * [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://arxiv.org/abs/2305.19725)
[2023-06-01]
736 | * [Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey](https://arxiv.org/abs/2312.10163)
[2023-12-19]
737 | * 上下文理解
738 | * [Context Understanding in Computer Vision: A Survey](https://arxiv.org/pdf/2302.05011.pdf)
[2023-02-13]
739 | * 多模态
740 | * [Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey](https://arxiv.org/abs/2302.10035)
[2023-02-21]
:star:[code](https://github.com/wangxiao5791509/MultiModal_BigModels_Survey)
741 | * [RGB-D And Thermal Sensor Fusion: A Systematic Literature Review](https://arxiv.org/abs/2305.11427)
[2023-05-22]
742 | * [Multimodal Sentiment Analysis: A Survey](https://arxiv.org/abs/2305.07611)
[2023-05-15]
743 | * [How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model](https://arxiv.org/abs/2311.07594)
[2023-11-15]
744 | * [A Survey of Reasoning with Foundation Models](https://arxiv.org/abs/2312.11562)
:star:[code](https://github.com/reasoning-survey/Awesome-Reasoning-Foundation-Models)
[2023-12-20]
745 | * [Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey](https://arxiv.org/abs/2312.16602)
[2023-12-29]
746 | * 运输物流与仓储
747 | * [Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing](https://arxiv.org/abs/2304.06009)
[2023-04-13]
:star:[code](https://a-nau.github.io/cv-in-logistics)
748 | * 事实检查
749 | * [Multimodal Automated Fact-Checking: A Survey](https://arxiv.org/abs/2305.13507)
[2023-05-24]
750 | * 匹配
751 | * [Confidence Intervals for Error Rates in Matching Tasks: Critical Review and Recommendations](https://arxiv.org/abs/2306.01198)
[2023-06-05]
752 | * 图像矢量化
753 | * [Image Vectorization: a Review](https://arxiv.org/abs/2306.06441)
[2023-06-13]
754 |
755 | ## 扫码CV君微信（注明：CV）入微信交流群：
756 |
757 | 
758 |
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/README.md:
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1 |
2 |
3 |
27 | 2月50篇。
28 | 3月45篇。
29 | 4月41篇。
30 |
31 | ## 目录
32 |
33 | |:cat:|:dog:|:tiger:|:wolf:|
34 | |------|------|------|------|
35 | |[1.Unkown(未分)](#1)|
36 |
37 |
38 | ## OOD
39 | * [Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey](http://arxiv.org/abs/2505.02448v1)
[2025-05-06]
40 |
41 | ## Machine Learning
42 | * [Machine Learning Applications to Diffuse Reflectance Spectroscopy in Optical Diagnosis; A Systematic Review](https://arxiv.org/abs/2503.02905)
[2025-03-06]
43 | * 强化学习
44 | * [Exploring Mutual Empowerment Between Wireless Networks and RL-based LLMs: A Survey](https://arxiv.org/abs/2503.09956)
[2025-03-14]
45 | * [Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey](https://arxiv.org/abs/2505.17352)
[2025-05-26]
46 | * 对比学习
47 | * [A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters](https://arxiv.org/abs/2502.08134)
[2025-02-13]
48 | * 类增量学习
49 | * [Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning](https://arxiv.org/abs/2502.08181)
[2025-02-13]
50 | * 对抗
51 | * [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://arxiv.org/abs/2503.00384)
[2025-03-04]
52 |
53 | ## agriculture(农业)
54 | * [A survey of datasets for computer vision in agriculture](https://arxiv.org/abs/2502.16950)
:star:[code](https://smartfarminglab.github.io/field_dataset_survey/)
[2025-02-25]
55 | * [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://arxiv.org/abs/2505.00805)
:star:[code](https://github.com/fadi-07/Awesome-Wheat-HSI-DeepLearning)
[2025-05-05]
56 | * [Vision Transformers in Precision Agriculture: A Comprehensive Survey](https://arxiv.org/abs/2504.21706)
[2025-05-01]
57 |
58 | ## Biomedical(生物特征识别)
59 | * 掌纹识别
60 | * [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://arxiv.org/abs/2501.01166)
[2025-01-03]
61 |
62 | ## Neural Radiance Fields
63 | * [Neural Radiance Fields for the Real World: A Survey](https://arxiv.org/abs/2501.13104)
[2025-01-23]
64 |
65 | ## Motion Generation(动作生成)
66 | * [Text-driven Motion Generation: Overview, Challenges and Directions](https://arxiv.org/abs/2505.09379)
[2025-05-15]
67 |
68 | ## Robots(机器人)
69 | * [Semantic Mapping in Indoor Embodied AI – A Comprehensive Survey and Future Directions](https://arxiv.org/abs/2501.05750)
[2025-01-13]
70 | * [OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation](https://arxiv.org/abs/2505.03912)
:star:[code](https://openhelix-robot.github.io/)
[2025-05-08]
71 | * [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://arxiv.org/abs/2505.20503)
[2025-05-28]
72 | * 位置识别
73 | * [Place Recognition: A Comprehensive Review, Current Challenges and Future Directions](https://arxiv.org/abs/2505.14068)
:star:[code](https://github.com/CV4RA/SOTA-Place-Recognitioner)
[2025-05-21]
74 | * 导航
75 | * [A Review of Vision-Based Assistive Systems for Visually Impaired People: Technologies, Applications, and Future Directions](https://arxiv.org/abs/2505.14298)
[2025-05-21]
76 |
77 | ## Industrial Defect Detection(工业缺陷检测)
78 | * [Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review](https://arxiv.org/abs/2501.11310)
[2025-01-22]
79 | * [A Survey on Industrial Anomalies Synthesis](https://arxiv.org/abs/2502.16412)
:star:[code](https://github.com/M-3LAB/awesome-anomaly-synthesis.)
[2025-02-25]
80 | * [A Survey on Foundation-Model-Based Industrial Defect Detection](https://arxiv.org/abs/2502.19106)
[2025-02-27]
81 |
82 | ## Video
83 | * [A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems](https://arxiv.org/abs/2502.06581)
[2025-02-11]
84 | * [Survey of Video Diffusion Models: Foundations, Implementations, and Applications](https://arxiv.org/abs/2504.16081)
:star:[code](https://github.com/Eyeline-Research/Survey-Video-Diffusion)
[2025-04-23]
85 | * 视频理解
86 | * [VideoLLM Benchmarks and Evaluation: A Survey](https://arxiv.org/abs/2505.03829)
[2025-05-08]
87 | * 视频监控
88 | * [Video Forgery Detection for Surveillance Cameras: A Review](https://arxiv.org/abs/2505.03832)
[2025-05-08]
89 |
90 | ## Action Detection(动作检测)
91 | * [Action Valuation in Sports: A Survey](https://arxiv.org/abs/2504.06163)
[2025-04-09]
92 | * [Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges](https://arxiv.org/abs/2505.03991)
[2025-05-08]
93 |
94 | ## Person Re-ID(重识别)
95 | * [Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review](https://arxiv.org/abs/2505.18401)
[2025-05-27]
96 | * [Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey](https://arxiv.org/abs/2505.20540)
[2025-05-28]
97 |
98 | ## Autonomous Driving(自动驾驶)
99 | * [A Survey of World Models for Autonomous Driving](https://arxiv.org/abs/2501.11260)
[2025-01-22]
100 | * [The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey](https://arxiv.org/abs/2502.10498)
:star:[code](https://github.com/LMD0311/Awesome-World-Model)
[2025-02-18]
101 | * [4D mmWave Radar in Adverse Environments for Autonomous Driving: A Survey](https://arxiv.org/abs/2503.24091)
[2025-04-01]
102 | * [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://arxiv.org/abs/2504.04631)
[2025-04-08]
103 | * [Adversarial Examples in Environment Perception for Automated Driving (Review)](https://arxiv.org/abs/2504.08414)
[2025-04-14]
104 | * [Collaborative Perception Datasets for Autonomous Driving: A Review](https://arxiv.org/abs/2504.12696)
:star:[code](https://github.com/frankwnb/Collaborative-Perception-Datasets-for-Autonomous-Driving)
[2025-04-18]
105 | * [Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends](https://arxiv.org/abs/2504.16134)
[2025-04-24]
106 | * [Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey](https://arxiv.org/abs/2505.00747)
[2025-05-05]
107 | * [Generative AI for Autonomous Driving: A Review](https://arxiv.org/abs/2505.15863)
[2025-05-23]
108 | * 车道线检测
109 | * [Datasets for Lane Detection in Autonomous Driving: A Comprehensive Review](https://arxiv.org/abs/2504.08540)
[2025-04-14]
110 | * 分心驾驶检测
111 | * [A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection](https://arxiv.org/abs/2501.11758)
[2025-01-22]
112 | * [Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques](http://arxiv.org/abs/2505.01973v1)
[2025-05-06]
113 | * 交通事故预测
114 | * [Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions](https://arxiv.org/abs/2505.07611)
[2025-05-13]
115 |
116 | ## Machine Learning
117 | * [A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application](https://arxiv.org/abs/2501.08585)
[2025-01-16]
118 |
119 | ## Few/Zero-Shot Learning/DG/A(小/零样本/域泛化/域适应)
120 | * 域泛化
121 | * [CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey](https://arxiv.org/abs/2504.14280)
:star:[code](https://github.com/jindongli-Ai/Survey_on_CLIP-Powered_Domain_Generalization_and_Adaptation)
[2025-04-22]
122 | * Non-Transferable Learning(反迁移学习)
123 | * [Toward Robust Non-Transferable Learning: A Survey and Benchmark](https://arxiv.org/abs/2502.13593)
[2025-02-20]
124 |
125 | ## Retrieval-Augmented Generation(检索增强生成)
126 | * [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://arxiv.org/abs/2503.18016)
:star:[code](https://github.com/zhengxuJosh/Awesome-RAG-Vision)
[2025-03-25]
127 |
128 | ## Vision-Language(视觉语言)
129 | * [Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability](https://arxiv.org/abs/2501.01346)
[2025-01-03]
130 | * [Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A Survey](https://arxiv.org/abs/2501.02189)
:star:[code](https://github.com/zli12321/Awesome-VLM-Papers-And-Models.git)
[2025-01-07]
131 | * [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://arxiv.org/abs/2501.03151)
[2025-01-07]
132 | * [Visual Large Language Models for Generalized and Specialized Applications](https://arxiv.org/abs/2501.02765)
:star:[code](https://github.com/JackYFL/awesome-VLLMs)
[2025-01-07]
133 | * [When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs](https://arxiv.org/abs/2502.06390)
:star:[code](https://github.com/AobtDai/VLM_Attack_Paper_List)
[2025-02-11]
134 | * [Survey on Vision-Language-Action Models](https://arxiv.org/abs/2502.06851)
[2025-02-12]
135 | * [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://arxiv.org/abs/2502.07855)
[2025-02-13]
136 | * [Harnessing Vision Models for Time Series Analysis: A Survey](https://arxiv.org/abs/2502.08869)
[2025-02-14]
137 | * [A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations](https://arxiv.org/abs/2502.14881)
:star:[code](https://github.com/XuankunRong/Awesome-LVLM-Safety)
[2025-02-24]
138 | * [Multi-Modal Foundation Models for Computational Pathology: A Survey](https://arxiv.org/abs/2503.09091)
[2025-03-13]
139 | * [Small Vision-Language Models: A Survey on Compact Architectures and Techniques](https://arxiv.org/abs/2503.10665)
[2025-03-17]
140 | * [A Survey on Efficient Vision-Language Models](https://arxiv.org/abs/2504.09724)
:star:[code](https://github.com/MPSC-UMBC/Efficient-Vision-Language-Models-A-Survey)
[2025-04-15]
141 | * [Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models](https://arxiv.org/abs/2505.04921)
:star:[code](https://github.com/HITsz-TMG/Awesome-Large-Multimodal-Reasoning-Models)
[2025-05-09]
142 | * LLM
143 | * [Leveraging Large Language Models For Scalable Vector Graphics Processing: A Review](https://arxiv.org/abs/2503.04983)
[2025-03-10]
144 | * [A Review on Large Language Models for Visual Analytics](https://arxiv.org/abs/2503.15176)
[2025-03-20]
145 | * [Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions](https://arxiv.org/abs/2503.16585)
[2025-03-24]
146 | * [How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM](https://arxiv.org/abs/2504.05786)
[2025-04-09]
147 | * [PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models](https://arxiv.org/abs/2504.14117)
:star:[code](https://github.com/Nusrat-Prottasha/PEFT-A2Z)
[2025-04-22]
148 | * [A Survey on (M)LLM-Based GUI Agents](https://arxiv.org/abs/2504.13865)
[2025-04-22]
149 | * MLLM
150 | * [Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review](https://arxiv.org/abs/2502.16586)
[2025-02-25]
151 | * [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://arxiv.org/abs/2503.12605)
:star:[code](https://github.com/yaotingwangofficial/Awesome-MCoT)
[2025-03-18]
152 | * [Aligning Multimodal LLM with Human Preference: A Survey](https://arxiv.org/abs/2503.14504)
:star:[code](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Alignment.)
[2025-03-19]
153 | * [Survey of Adversarial Robustness in Multimodal Large Language Models](https://arxiv.org/abs/2503.13962)
[2025-03-19]
154 |
155 | ## GAN/Image Synthesis(图像生成)
156 | * [Generative AI for Cel-Animation: A Survey](https://arxiv.org/abs/2501.06250)
:star:[code](https://github.com/yunlong10/Awesome-AI4Animation)
[2025-01-14]
157 | * [Generative Physical AI in Vision: A Survey](https://arxiv.org/abs/2501.10928)
:star:[code](https://github.com/BestJunYu/Awesome-Physics-aware-Generation)
[2025-01-22]
158 | * [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://arxiv.org/abs/2502.05240)
[2025-02-11]
159 | * [A Survey on Text-Driven 360-Degree Panorama Generation](https://arxiv.org/abs/2502.14799)
:star:[code](https://littlewhitesea.github.io/Text-Driven-Pano-Gen/)
[2025-02-21]
160 | * [Methods and Trends in Detecting Generated Images: A Comprehensive Review](https://arxiv.org/abs/2502.15176)
[2025-02-24]
161 | * [Simulating the Real World: A Unified Survey of Multimodal Generative Models](https://arxiv.org/abs/2503.04641)
[2025-03-07]
162 | * [Generative AI for Film Creation: A Survey of Recent Advances](https://arxiv.org/abs/2504.08296)
[2025-04-14]
163 | * [Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression](https://arxiv.org/abs/2505.19398)
[2025-05-27]
164 | * GAN
165 | * [Image Inversion: A Survey from GANs to Diffusion and Beyond](https://arxiv.org/abs/2502.11974)
:star:[code](https://github.com/RyanChenYN/ImageInversion)
[2025-02-18]
166 | * [Generative Adversarial Networks with Limited Data: A Survey and Benchmarking](https://arxiv.org/abs/2504.05456)
[2025-04-09]
167 | * 图像生成
168 | * [Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing](https://arxiv.org/abs/2502.07829)
[2025-02-13]
169 | * [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://arxiv.org/abs/2502.13081)
:star:[code](https://github.com/csyxwei/Awesome-Personalized-Image-Generation)
[2025-02-19]
170 | * AIGC
171 | * [Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC](https://arxiv.org/abs/2502.07007)
[2025-02-12]
172 | * [Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions](https://arxiv.org/abs/2504.19056)
:star:[code](https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey)
[2025-04-29]
173 | * 图像到图像翻译
174 | * [Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review](https://arxiv.org/abs/2502.08667)
[2025-02-14]
175 | * 文本-图像
176 | * [A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models](https://arxiv.org/abs/2502.14896)
[2025-02-24]
177 | * [A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images](https://arxiv.org/abs/2502.21151)
[2025-03-03]
178 | * [A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safety](https://arxiv.org/abs/2503.00020)
[2025-03-04]
179 | * [A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis](https://arxiv.org/abs/2503.11101)
[2025-03-17]
180 | * [A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models](https://arxiv.org/abs/2503.13576)
[2025-03-19]
181 | * [Text to Image Generation and Editing: A Survey](http://arxiv.org/abs/2505.02527v1)
[2025-05-06]
182 | * 视频生成
183 | * [A Survey: Spatiotemporal Consistency in Video Generation](https://arxiv.org/abs/2502.17863)
[2025-02-26]
184 | * [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://arxiv.org/abs/2503.21765)
:star:[code](https://github.com/minnie-lin/Awesome-Physics-Cognition-based-Video-Generation)
[2025-03-28]
185 | * [A Survey of Interactive Generative Video](https://arxiv.org/abs/2504.21853)
[2025-05-01]
186 | * 4D生成
187 | * [Advances in 4D Generation: A Survey](https://arxiv.org/abs/2503.14501)
:star:[code](https://github.com/MiaoQiaowei/Awesome-4D)
[2025-03-19]
188 | * 3D生成
189 | * [Recent Advance in 3D Object and Scene Generation: A Survey](https://arxiv.org/abs/2504.11734)
[2025-04-17]
190 | * 视觉-音乐生成
191 | * [Vision-to-Music Generation: A Survey](https://arxiv.org/abs/2503.21254)
:star:[code](https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.)
[2025-03-28]
192 | * 场景生成
193 | * [3D Scene Generation: A Survey](https://arxiv.org/abs/2505.05474)
:star:[code](https://github.com/hzxie/Awesome-3D-Scene-Generation)
[2025-05-09]
194 |
195 | ## MC/KD/Pruning(模型压缩/知识蒸馏/剪枝)
196 | * [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://arxiv.org/abs/2501.07451)
[2025-01-14]
197 | * [Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies](https://arxiv.org/abs/2503.02891)
[2025-03-06]
198 | * [Image Recognition with Online Lightweight Vision Transformer: A Survey](https://arxiv.org/abs/2505.03113)
:star:[code](https://github.com/ajxklo/Lightweight-VIT)
[2025-05-07]
199 | * 量化
200 | * [Zero-shot Quantization: A Comprehensive Survey](https://arxiv.org/abs/2505.09188)
[2025-05-15]
201 | * KD
202 | * [A Comprehensive Survey on Knowledge Distillation](https://arxiv.org/abs/2503.12067)
:star:[code](https://github.com/IPL-Sharif/KD_Survey)
[2025-03-18]
203 |
204 | ## Visual Question Answering (视觉问答)
205 | * [Visual question answering: from early developments to recent advances -- a survey](https://arxiv.org/abs/2501.03939)
[2025-01-08]
206 | * [The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering](https://arxiv.org/abs/2501.07109)
[2025-01-14]
207 | * [A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task](https://arxiv.org/abs/2504.17547)
[2025-04-25]
208 |
209 | ## Medical Image Progress(医学图像处理)
210 | * [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://arxiv.org/abs/2501.10727)
[2025-01-22]
211 | * [Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact](https://arxiv.org/abs/2502.08333)
[2025-02-13]
212 | * [A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://arxiv.org/abs/2502.11211)
[2025-02-18]
213 | * [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://arxiv.org/abs/2502.14935)
[2025-02-24]
214 | * [The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances](https://arxiv.org/abs/2503.14546)
[2025-03-20]
215 | * [Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging](https://arxiv.org/abs/2503.16543)
[2025-03-24]
216 | * [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://arxiv.org/abs/2504.11588)
[2025-04-17]
217 | * [A Comprehensive Review on RNA Subcellular Localization Prediction](https://arxiv.org/abs/2504.17162)
[2025-04-25]
218 | * [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://arxiv.org/abs/2505.00525)
[2025-05-02]
219 | * [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://arxiv.org/abs/2505.03599)
[2025-05-07]
220 | * [Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers](https://arxiv.org/abs/2505.02843)
[2025-05-07]
221 | * [The Eye as a Window to Systemic Health: A Survey of Retinal Imaging from Classical Techniques to Oculomics](https://arxiv.org/abs/2505.04006)
[2025-05-08]
222 | * [The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review](https://arxiv.org/abs/2505.06118)
[2025-05-12]
223 | * [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://arxiv.org/abs/2505.07866)
[2025-05-14]
224 | * [Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://arxiv.org/abs/2505.10993)
[2025-05-19]
225 | * 医学图像分割
226 | * [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://arxiv.org/abs/2502.06895)
[2025-02-12]
227 | * [Recent Advances in Medical Imaging Segmentation: A Survey](https://arxiv.org/abs/2505.09274)
:star:[code](https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation)
[2025-05-15]
228 | * 医学图像融合
229 | * [A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis](https://arxiv.org/abs/2505.14715)
[2025-05-22]
230 | * 手术场景理解
231 | * [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://arxiv.org/abs/2502.14886)
[2025-02-24]
232 | * 手术视频分割
233 | * [Deep learning approaches to surgical video segmentation and object detection: A Scoping Review](https://arxiv.org/abs/2502.16459)
[2025-02-25]
234 | * 图像配准
235 | * [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://arxiv.org/abs/2502.19123)
[2025-02-27]
236 | * MRI重建
237 | * [A Survey of fMRI to Image Reconstruction](https://arxiv.org/abs/2502.16861)
[2025-02-25]
238 | * [A Comprehensive Survey on Magnetic Resonance Image Reconstruction](https://arxiv.org/abs/2503.07097)
[2025-03-11]
239 | * [A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli](https://arxiv.org/abs/2503.15978)
:star:[code](https://github.com/LpyNow/BrainDecodingImage)
[2025-03-21]
240 |
241 | ## OCR
242 | * [Handwritten Text Recognition: A Survey](https://arxiv.org/abs/2502.08417)
[2025-02-13]
243 | * [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://arxiv.org/abs/2504.21682)
:star:[code](https://github.com/shuyansy/Visual-Text-Processing-survey)
[2025-05-01]
244 |
245 | ## UAV/Remote Sensing/Satellite Image(无人机/遥感/卫星图像)
246 | * [Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites](https://arxiv.org/abs/2501.12030)
[2025-01-22]
247 | * [Plantation Monitoring Using Drone Images: A Dataset and Performance Review](https://arxiv.org/abs/2502.08233)
[2025-02-13]
248 | * [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://arxiv.org/abs/2503.22081)
[2025-03-31]
249 | * [A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities](https://arxiv.org/abs/2504.00901)
:star:[code](https://github.com/yc-cui/Deep-Learning-Spatiotemporal-Fusion-Survey)
[2025-04-02]
250 | * [MIMRS: A Survey on Masked Image Modeling in Remote Sensing](https://arxiv.org/abs/2504.03181)
[2025-04-07]
251 | * [A comprehensive review of remote sensing in wetland classification and mapping](https://arxiv.org/abs/2504.10842)
[2025-04-16]
252 | * [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://arxiv.org/abs/2505.00630)
:star:[code](https://github.com/BaoBao0926/Awesome-Mamba-in-Remote-Sensing)
[2025-05-02]
253 | * [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://arxiv.org/abs/2505.14361)
[2025-05-21]
254 | * Anti-UAV
255 | * [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://arxiv.org/abs/2504.11967)
[2025-04-17]
256 | * 变化检测
257 | * [Operational Change Detection for Geographical Information: Overview and Challenges](https://arxiv.org/abs/2503.14109)
[2025-03-19]
258 | * 船舶分类
259 | * [A Survey on SAR ship classification using Deep Learning](https://arxiv.org/abs/2503.11906)
[2025-03-18]
260 | * 火灾烟雾
261 | [Fire and Smoke Datasets in 20 Years: An In-depth Review](https://arxiv.org/abs/2503.14552)
[2025-03-20]
262 | * 野生动物监测
263 | * [Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys](https://arxiv.org/abs/2505.10737)
[2025-05-19]
264 | * 遥感图像分割
265 | * [From Pixels to Images: Deep Learning Advances in Remote Sensing Image Semantic Segmentation](https://arxiv.org/abs/2505.15147)
[2025-05-22]
266 | * 遥感超分辨率
267 | * [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://arxiv.org/abs/2505.23248)
[2025-05-30]
268 |
269 | ## Object Detection(目标检测)
270 | * [YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review](https://arxiv.org/abs/2501.13400)
[2025-01-24]
271 | * [Context in object detection: a systematic literature review](https://arxiv.org/abs/2503.23249)
[2025-04-01]
272 | * [Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation](https://arxiv.org/abs/2504.09480)
:star:[code](https://github.com/better-chao/perceptual_abilities_evaluation)
[2025-04-15]
273 | * [A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions](https://arxiv.org/abs/2504.11995)
[2025-04-17]
274 | * [A Decade of You Only Look Once (YOLO) for Object Detection](https://arxiv.org/abs/2504.18586)
[2025-04-29]
275 | * 线路检测
276 | * [Deep Learning in Automated Power Line Inspection: A Review](https://arxiv.org/abs/2502.07826)
[2025-02-13]
277 | * 小目标检测
278 | * [Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications](https://arxiv.org/abs/2503.20516)
[2025-03-27]
279 | * 3D目标检测
280 | * [A Review of 3D Object Detection with Vision-Language Models](https://arxiv.org/abs/2504.18738)
[2025-04-29]
281 |
282 | ## HOI
283 | * [3D Human Interaction Generation: A Survey](https://arxiv.org/abs/2503.13120)
[2025-03-18]
284 | * [A Survey on Human Interaction Motion Generation](https://arxiv.org/abs/2503.12763)
:star:[code](https://github.com/soraproducer/Awesome-Human-Interaction-Motion-Generation)
[2025-03-18]
285 |
286 | ## Action Recognition
287 | * [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://arxiv.org/abs/2501.13066)
[2025-01-23]
288 |
289 | ## Pose(姿态估计)
290 | * [Survey on Hand Gesture Recognition from Visual Input](https://arxiv.org/abs/2501.11992)
[2025-01-22]
291 |
292 | ## Points Cloud(点云)
293 | * [Implicit Guidance and Explicit Representation of Semantic Information in Points Cloud: A Survey](https://arxiv.org/abs/2501.05473)
[2025-01-13]
294 | * [Point Cloud Based Scene Segmentation: A Survey](https://arxiv.org/abs/2503.12595)
[2025-03-18]
295 |
296 | ## 3D Visual
297 | * 三维重建
298 | * [Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison](https://arxiv.org/abs/2502.20154)
[2025-02-28]
299 | * [Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey](https://arxiv.org/abs/2503.14537)
[2025-03-20]
300 | * [A Survey on Event-driven 3D Reconstruction: Development under Different Categories](https://arxiv.org/abs/2503.19753)
[2025-03-26]
301 | * [Explicit and Implicit Representations in AI-based 3D Reconstruction for Radiology: A systematic literature review](https://arxiv.org/abs/2504.11349)
:star:[code](https://github.com/Bean-Young/AI4Med)
[2025-04-16]
302 | * [A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond](https://arxiv.org/abs/2505.00737)
[2025-05-05]
303 | * [A Survey of 3D Reconstruction with Event Cameras: From Event-based Geometry to Neural 3D Rendering](https://arxiv.org/abs/2505.08438)
[2025-05-14]
304 | * 深度估计
305 | * [A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision](https://arxiv.org/abs/2501.05147)
[2025-01-10]
306 | * [Survey on Monocular Metric Depth Estimation](https://arxiv.org/abs/2501.11841)
[2025-01-22]
307 |
308 | ## Face
309 | * [A Survey on Facial Image Privacy Preservation in Cloud-Based Services](https://arxiv.org/abs/2501.08665)
[2025-01-16]
310 | * [Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities](https://arxiv.org/abs/2502.06803)
[2025-02-12]
311 | * [Face Deepfakes - A Comprehensive Review](https://arxiv.org/abs/2502.09812)
[2025-02-17]
312 | * 情绪分析
313 | * [Enhanced Sentiment Analysis of Iranian Restaurant Reviews Utilizing Sentiment Intensity Analyzer & Fuzzy Logic](https://arxiv.org/abs/2503.12141)
[2025-03-18]
314 | * 情感识别
315 | * [Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework](https://arxiv.org/abs/2505.18175)
[2025-05-27]
316 |
317 | ## Image Segmentation(图像分割)
318 | * [A Comparative Review of the Histogram-based Image Segmentation Methods](https://arxiv.org/abs/2502.18550)
[2025-02-27]
319 | * [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://arxiv.org/abs/2503.12781)
[2025-03-18]
320 | * [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://arxiv.org/abs/2505.13584)
[2025-05-21]
321 | * [Reasoning Segmentation for Images and Videos: A Survey](https://arxiv.org/abs/2505.18816)
[2025-05-27]
322 | * 语义分割
323 | * [A Survey on Training-free Open-Vocabulary Semantic Segmentation](https://arxiv.org/abs/2505.22209)
[2025-05-29]
324 |
325 | ## Image Retrieval(图像检索)
326 | * [A Comprehensive Survey on Composed Image Retrieval](https://arxiv.org/abs/2502.18495)
[2025-02-27]
327 | * [Composed Multi-modal Retrieval: A Survey of Approaches and Applications](https://arxiv.org/abs/2503.01334)
[2025-03-04]
328 |
329 |
330 | ## Image Classification
331 | * [Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh's Perspective](https://arxiv.org/abs/2501.03305)
[2025-01-08]基于深度学习的植物叶片病害检测与分类
332 |
333 | ## Image Super-Resolution
334 | * [State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications](https://arxiv.org/abs/2501.07855)
[2025-01-15]
335 |
336 | ## Image Progress(图像/视频处理)
337 | * 图像恢复
338 | * [Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey](https://arxiv.org/abs/2505.16161)
:star:[code](https://github.com/wlydlut/UHD-Image-Restoration-Survey)
[2025-05-23]
339 | * 水下图像增强
340 | * [Underwater Image Enhancement using Generative Adversarial Networks: A Survey](https://arxiv.org/abs/2501.06273)
[2025-01-14]
341 | * [Visual enhancement and 3D representation for underwater scenes: a review](http://arxiv.org/abs/2505.01869v1)
[2025-05-06]
342 | * 图像质量评估/增强
343 | * [Fundus Image Quality Assessment and Enhancement: a Systematic Review](https://arxiv.org/abs/2501.11520)
[2025-01-22]
344 | * [A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement](https://arxiv.org/abs/2502.05995)
[2025-02-11]
345 | * [A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook](https://arxiv.org/abs/2502.08540)
[2025-02-13]
346 | * [A review of advancements in low-light image enhancement using deep learning](https://arxiv.org/abs/2505.05759)
[2025-05-12]
347 | * 去反射
348 | * [Survey on Single-Image Reflection Removal using Deep Learning Techniques](https://arxiv.org/abs/2502.08836)
[2025-02-14]
349 |
350 | ## Unknown(未分)
351 | * [Visualizing Uncertainty in Image Guided Surgery a Review](https://arxiv.org/abs/2501.06280)
[2025-01-14]
352 | * [A Preliminary Survey of Semantic Descriptive Model for Images](https://arxiv.org/abs/2501.08352)
[2025-01-16]
353 | * [New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends](https://arxiv.org/abs/2501.10324)
[2025-01-20]
354 | * [Explainable artificial intelligence (XAI): from inherent explainability to large language models](https://arxiv.org/abs/2501.09967)
[2025-01-20]
355 | * [Explainability for Vision Foundation Models: A Survey](https://arxiv.org/abs/2501.12203)
[2025-01-22]
356 | * [Advanced technology in railway track monitoring using the GPR Technique: A Review](https://arxiv.org/abs/2501.11132)
[2025-01-22]
357 | * [Reproducibility review of "Why Not Other Classes": Towards Class-Contrastive Back-Propagation Explanations](https://arxiv.org/abs/2501.11096)
[2025-01-22]
358 | * [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://arxiv.org/abs/2502.05151)
[2025-02-10]
359 | * [Diffusion Models for Computational Neuroimaging: A Survey](https://arxiv.org/abs/2502.06552)
:star:[code](https://github.com/JoeZhao527/dm4neuro)
[2025-02-11]
360 | * [Safety at Scale: A Comprehensive Survey of Large Model Safety](https://arxiv.org/abs/2502.05206)
[2025-02-11]
361 | * [Event Vision Sensor: A Review](https://arxiv.org/abs/2502.06116)
[2025-02-11]
362 | * [A Survey on Mamba Architecture for Vision Applications](https://arxiv.org/abs/2502.07161)
[2025-02-12]
363 | * [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://arxiv.org/abs/2502.10444)
:star:[code](https://github.com/52CV/CV-Surveys/)
[2025-02-18]
364 | * [Event-based Solutions for Human-centered Applications: A Comprehensive Review](https://arxiv.org/abs/2502.18490)
:star:[code](https://github.com/nmirabeth/event_human)
[2025-02-27]
365 | * [A Survey on Ordinal Regression: Applications, Advances and Prospects](https://arxiv.org/abs/2503.00952)
[2025-03-04]
366 | * [Lossy Neural Compression for Geospatial Analytics: A Review](https://arxiv.org/abs/2503.01505)
[2025-03-04]
367 | * [A Review on Geometry and Surface Inspection in 3D Concrete Printing](https://arxiv.org/abs/2503.07472)
[2025-03-11]
368 | * [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://arxiv.org/abs/2503.07276)
[2025-03-11]
369 | * [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://arxiv.org/abs/2503.08008)
[2025-03-12]
370 | * [Challenges and Trends in Egocentric Vision: A Survey](https://arxiv.org/abs/2503.15275)
[2025-03-20]
371 | * [A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions](https://arxiv.org/abs/2503.16546)
[2025-03-24]
372 | * [Hybrid Multi-Stage Learning Framework for Edge Detection: A Survey](https://arxiv.org/abs/2503.21827)
[2025-03-31]
373 | * [Towards Mobile Sensing with Event Cameras on High-mobility Resource-constrained Devices: A Survey](https://arxiv.org/abs/2503.22943)
[2025-04-01]
374 | * [Foundation Models For Seismic Data Processing: An Extensive Review](https://arxiv.org/abs/2503.24166)
[2025-04-01]
375 | * [A Survey of Pathology Foundation Model: Progress and Future Directions](https://arxiv.org/abs/2504.04045)
:star:[code](https://github.com/BearCleverProud/AwesomeWSI)
[2025-04-08]
376 | * [Attention in Diffusion Model: A Survey](https://arxiv.org/abs/2504.03738)
[2025-04-08]
377 | * [Loss Functions in Deep Learning: A Comprehensive Review](https://arxiv.org/abs/2504.04242)
[2025-04-08]
378 | * [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://arxiv.org/abs/2504.08588)
[2025-04-14]
379 | * [Computer-Aided Layout Generation for Building Design: A Review](https://arxiv.org/abs/2504.09694)
:star:[code](https://github.com/jcliu0428/awesome-building-layout-generation)
[2025-04-15]
380 | * [Digital Twin Generation from Visual Data: A Survey](https://arxiv.org/abs/2504.13159)
:star:[code](https://github.com/ndrwmlnk/awesome-digital-twins)
[2025-04-18]
381 | * [A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions](https://arxiv.org/abs/2504.14800)
[2025-04-22]
382 | * [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://arxiv.org/abs/2504.16972)
[2025-04-25]
383 | * [A Survey on Event-based Optical Marker Systems](https://arxiv.org/abs/2504.20736)
[2025-04-30]
384 | * [Diffusion Model Quantization: A Review](https://arxiv.org/abs/2505.05215)
:star:[code](https://github.com/TaylorJocelyn/Diffusion-Model-Quantization)
[2025-05-09]
385 | * [From Events to Enhancement: A Survey on Event-Based Imaging Technologies](https://arxiv.org/abs/2505.05488)
:star:[code](https://github.com/yunfanLu/Awesome-Event-Imaging)
[2025-05-12]
386 | * [Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence](https://arxiv.org/abs/2505.06907)
[2025-05-13]
387 | * [A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?](https://arxiv.org/abs/2505.10924)
[2025-05-19]
388 | * [Diffusion Model in Hyperspectral Image Processing and Analysis: A Review](https://arxiv.org/abs/2505.11158)
[2025-05-19]
389 | * [Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey](https://arxiv.org/abs/2505.14340)
[2025-05-21]
390 | * [Semantic Correspondence: Unified Benchmarking and a Strong Baseline](https://arxiv.org/abs/2505.18060)
:star:[code](https://github.com/Visual-AI/Semantic-Correspondence)
[2025-05-26]
391 |
392 |
393 |
394 | ## 2023 年论文分类汇总戳这里
395 | ↘️[CVPR-2023-Papers](https://github.com/52CV/CVPR-2023-Papers)
396 | ↘️[WACV-2023-Papers](https://github.com/52CV/WACV-2023-Papers)
397 | ↘️[ICCV-2023-Papers](https://github.com/52CV/ICCV-2023-Papers)
398 | ↘️[2023-CV-Surveys](https://github.com/52CV/CV-Surveys/blob/main/2023-CV-Surveys.md)
399 |
400 |
401 |
402 | ## 2022 年论文分类汇总戳这里
403 | ↘️[CVPR-2022-Papers](https://github.com/52CV/CVPR-2022-Papers/blob/main/README.md)
404 | ↘️[WACV-2022-Papers](https://github.com/52CV/WACV-2022-Papers)
405 | ↘️[ECCV-2022-Papers](https://github.com/52CV/ECCV-2022-Papers/blob/main/README.md)
406 |
407 |
408 |
409 | ## 2021 年论文分类汇总戳这里
410 | ↘️[ICCV-2021-Papers](https://github.com/52CV/ICCV-2021-Papers)
411 | ↘️[CVPR-2021-Papers](https://github.com/52CV/CVPR-2021-Papers)
412 |
413 |
414 |
415 | ## 2020 年论文分类汇总戳这里
416 | ↘️[CVPR-2020-Papers](https://github.com/52CV/CVPR-2020-Papers)
417 | ↘️[ECCV-2020-Papers](https://github.com/52CV/ECCV-2020-Papers)
418 |
419 | ## 扫码CV君微信（注明：CV）入微信交流群：
420 |
421 | 
422 |
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