├── .gitignore ├── LICENSE ├── PretendX └── Readme.md └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | *~ 2 | Paper/ 3 | .idea/ 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Shin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /PretendX/Readme.md: -------------------------------------------------------------------------------- 1 | # Some expressions used in papers to pretend X 2 | 3 | The expressions are sorted by lexicographic order. 4 | 5 | [A](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#a) | [B](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#b) | [C](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#c) | [D](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#d) | [E](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#e) | [F](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#f) | [G](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#g) | [H](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#h) | [I](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#i) | [J](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#j) | [K](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#k) | [L](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#l) | [M](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#m) | [N](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#n) | [O](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#o) | [P](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#p) | [Q](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#q) | [R](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#r) | [S](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#s) | [T](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#t) | [U](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#u) | [V](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#v) | [W](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#w) | [X](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#x) | [Y](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#y) | [Z](https://github.com/shinshiner/Paper-Survey/tree/master/PretendX#z) 6 | 7 | ## A 8 | 9 | * aggregate | v. 合计（i.e add up） 10 | * analogously | adv. 类似地 11 | * ascertain | v. 探明 12 | * auxiliary | adj. 辅助的；附加的 13 | * ablation experiments | 消融实验 14 | * a high variety of | 种类繁多 15 | 16 | ## B 17 | 18 | * be capable of | 有能力的 19 | * be capped at | 控制在...内；以...为上限 20 | * be deemed to | 被视为 21 | * be particularly prone to ... | 特别容易... 22 | 23 | ## C 24 | 25 | * cue | n. 暗示； v. 隐射（i.e imply） 26 | * confirm | v. 确认 27 | * concatenation | n. 级联；并列 28 | * consecutive | adj. 连续的 29 | 30 | ## D 31 | 32 | * decisive | adj. 决定性的 33 | * deformable | adj. 可变形的 34 | * depict | v. (i.e describe) 35 | * deploy | v. 部署 36 | * devise | v. 设计；发明 37 | * dilate | v. 膨胀；使膨胀 38 | * discard | v. 抛弃；放弃 39 | * discrepancy | n. 差异 40 | * dominate | v. 支配；主导 41 | * draw upon the ideas | 借鉴这些想法 42 | 43 | ## E 44 | 45 | * elapse | v. （时间）过去，流逝 46 | * elicit | v. 抽出（i.e extract）；引出（i.e lead to） 47 | * eliminate | v. 消除 48 | * empirically | adv. 经验地 49 | * engender | v. 产生 50 | * exceedingly | adv. 非常 51 | * explicitly | adv. 明确地 52 | * encompass | v. 环绕（i.e surround） 53 | 54 | ## F 55 | 56 | * facilitate | v. 促进；便利 57 | * feasibility | n. 可行性；可能性 58 | * for the sake of | 为了 59 | * for the sake of brevity | 为了简洁起见 60 | 61 | ## H 62 | 63 | * heuristics | n. 启发式 64 | * holistically | adv. 从整体上 65 | * homogeneous | adj. 均匀的；同质的 66 | * hurdles | n. 障碍 67 | * hand-crafted | 手工制作 68 | 69 | ## I 70 | 71 | * illustrate | v. 图解说明 72 | * implicitly | adv. 隐式地 73 | * impose | v. 强加 74 | * incentivize | n. 激励 75 | * inconsistent | adj. 不符的；不一致的 76 | * incredible | adj. 难以置信的 77 | * infeasible | adj. 不可行的 78 | * interpret | v. 解释；阐释 79 | * in particular | 尤其是 80 | * in spite of | 尽管 81 | * in the presence of ... | 在...的存在下 82 | * in the realm of | 在...领域内 83 | 84 | ## L 85 | 86 | * laborious | adj. 费力的 87 | 88 | ## M 89 | 90 | * moreover | adv. 此外 91 | * make major inroads into | 取得了重大进展 92 | 93 | ## N 94 | 95 | * notion | n. 概念 (i.e concept) 96 | * non-trivial | adj. (i.e significant) 97 | 98 | ## O 99 | 100 | * omit | v. 忽略 101 | 102 | ## P 103 | 104 | * perceive | v. 感知；察觉；看出 105 | * perturbation | n. 扰动 106 | * plausible | adj. 貌似可信的；貌似有理的 107 | * prior | adj. 先前的 108 | * probe | v. 探测；探索 109 | * partitioned ... into ... | 把 ... 分成 ... 110 | * performs favorably against | 表现出色 111 | * perform on par with | 与 ... 相提并论 112 | * put forth the proposition | 提出这个命题 113 | 114 | ## R 115 | 116 | * refine | v. 改进；提炼 117 | * reside in | 在 118 | 119 | ## S 120 | 121 | * scenarios | n. 场景 122 | * snapshot | n. 快照 123 | * succinctly | adv. 简洁地 124 | * sufficient | adj. 足够的 125 | * scale up | 放大；扩大 126 | * segment a scene into its constituent objects | 将场景分割为其组成对象 127 | * seminal work | 开创性的工作 128 | * surprisingly good | 出奇的好 129 | 130 | ## T 131 | 132 | * to this end | 为此（i.e for this reason） 133 | * the abundance of | 丰富的 134 | * the caveat is that | 需要注意的是 135 | 136 | ## V 137 | 138 | * vast amount of | 大量的 139 | 140 | ## W 141 | 142 | * with respect to | 关于 143 | * with the aid of | 在...的帮助下 144 | 145 | ## Y 146 | 147 | * yield | v. 产生 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | The classification of articles may be inaccurate due to personal limited knowledge level. 2 | 3 | # Content 4 | * [Data Structure](https://github.com/shinshiner/Paper-Survey#data-structure) 5 | * [Tree](https://github.com/shinshiner/Paper-Survey#tree) 6 | * [Machine Learning](https://github.com/shinshiner/Paper-Survey#machine-learning) 7 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm) 8 | * [Neural Network](https://github.com/shinshiner/Paper-Survey#neural-network) 9 | * [Framework](https://github.com/shinshiner/Paper-Survey#framework) 10 | * [Network Architecture](https://github.com/shinshiner/Paper-Survey#network-architecture) 11 | * [Neural Network Component](https://github.com/shinshiner/Paper-Survey#neural-network-component) 12 | * [Optimizer](https://github.com/shinshiner/Paper-Survey#optimizer) 13 | * [Computer Vision](https://github.com/shinshiner/Paper-Survey#computer-vision) 14 | * [Dataset](https://github.com/shinshiner/Paper-Survey#dataset) 15 | * [2D Object Detection](https://github.com/shinshiner/Paper-Survey#2d-object-detection) 16 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-1) 17 | * [3D Object Detection](https://github.com/shinshiner/Paper-Survey#3d-object-detection) 18 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-2) 19 | * [2D Segmentation](https://github.com/shinshiner/Paper-Survey#2d-segmentation) 20 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-3) 21 | * [3D Segmentation](https://github.com/shinshiner/Paper-Survey#3d-segmentation) 22 | *   [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-4) 23 | * [2D Pose](https://github.com/shinshiner/Paper-Survey#2d-pose) 24 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-5) 25 | * [3D Pose](https://github.com/shinshiner/Paper-Survey#3d-pose) 26 | *   [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-6) 27 | * [Video](https://github.com/shinshiner/Paper-Survey#video) 28 | * [Segmentation](https://github.com/shinshiner/Paper-Survey#segmentation) 29 | * [Motion Representation](https://github.com/shinshiner/Paper-Survey#motion-representation) 30 | * [Generative Model](https://github.com/shinshiner/Paper-Survey#generative-model) 31 | * [VAE (Variational Auto-Encoder)](https://github.com/shinshiner/Paper-Survey#vae-variational-auto-encoder) 32 | * [Models](https://github.com/shinshiner/Paper-Survey#models) 33 | * [Applications](https://github.com/shinshiner/Paper-Survey#applications) 34 | * [GAN (Generative Adversarial Networks)](https://github.com/shinshiner/Paper-Survey#gan-generative-adversarial-networks) 35 | * [Models](https://github.com/shinshiner/Paper-Survey#models-1) 36 | * [Applications](https://github.com/shinshiner/Paper-Survey#applications-1) 37 | * [Reinforcement Learning](https://github.com/shinshiner/Paper-Survey#reinforcement-learning) 38 | * [Environment](https://github.com/shinshiner/Paper-Survey#environment) 39 | * [Algorithm](https://github.com/shinshiner/Paper-Survey#algorithm-7) 40 | * [RL in Games](https://github.com/shinshiner/Paper-Survey#rl-in-games) 41 | * [Distributional RL](https://github.com/shinshiner/Paper-Survey#distributional-rl) 42 | * [Transfer Learning & Meta Learning](https://github.com/shinshiner/Paper-Survey#transfer-learning--meta-learning) 43 | * [Algorithm or Model](https://github.com/shinshiner/Paper-Survey#algorithm-or-model) 44 | * [Zero Shot Learning](https://github.com/shinshiner/Paper-Survey#zero-shot-learning) 45 | * [Robot](https://github.com/shinshiner/Paper-Survey#robot) 46 | * [Dataset](https://github.com/shinshiner/Paper-Survey#dataset-1) 47 | * [Hardware](https://github.com/shinshiner/Paper-Survey#hardware) 48 | * [Grasping](https://github.com/shinshiner/Paper-Survey#grasping) 49 | * [Grasping with RL](https://github.com/shinshiner/Paper-Survey#grasping-with-rl) 50 | * [Grasping Unknown Objects](https://github.com/shinshiner/Paper-Survey#grasping-unknown-objects) 51 | * [Grasping in Cluttered Environment](https://github.com/shinshiner/Paper-Survey#grasping-in-cluttered-environment) 52 | * [Grasping via Segmentation](https://github.com/shinshiner/Paper-Survey#grasping-via-segmentation) 53 | * [Grasping Points Selection](https://github.com/shinshiner/Paper-Survey#grasping-points-selection) 54 | * [Machine Vision](https://github.com/shinshiner/Paper-Survey#machine-vision) 55 | * [Active Perception](https://github.com/shinshiner/Paper-Survey#active-perception) 56 | * [Motion Prediction](https://github.com/shinshiner/Paper-Survey#motion-prediction) 57 | * [Interactive Perception](https://github.com/shinshiner/Paper-Survey#interactive-perception) 58 | 59 | # Data Structure 60 | 61 | ## Tree 62 | 63 | * 【Kd-tree】[Multidimensional binary search trees used for associative searching](https://dl.acm.org/citation.cfm?id=361007) (1975) 64 | 65 | * 【Oc-tree】[Octree encoding: A new technique for the representation, manipulation and display of arbitrary 3-d objects by computer](https://www.researchgate.net/publication/238720460_Octree_Encoding_A_New_Technique_for_the_Representation_Manipulation_and_Display_of_Arbitrary_3-D_Objects_by_Computer) (1980) 66 | 67 | # Machine Learning 68 | 69 | ## Algorithm 70 | 71 | * 【SVM】[Least squares support vector machine classifiers](https://lirias.kuleuven.be/bitstream/123456789/218716/2/Suykens_NeurProcLett.pdf) (**Springer** 1999) 72 | 73 | * 【PCA】[Singular value decomposition and principal component analysis](https://link.springer.com/chapter/10.1007%2F0-306-47815-3_5) (**Springer** 2003) 74 | 75 | # Neural Network 76 | 77 | ## Framework 78 | 79 | * [Torch7: A MATLAB-like environment for machine learning](https://infoscience.epfl.ch//record/192376/files/Collobert_NIPSWORKSHOP_2011.pdf) (**NIPS workshop** 2011) 80 | 81 | * [Caffe: Convolutional Architecture for Fast Feature Embedding](https://dl.acm.org/citation.cfm?id=2654889) (**arxiv** 2014) 82 | 83 | * [TensorFlow: A System for Large-Scale Machine Learning](https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf) (**OSDI** 2016) 84 | 85 | ## Network Architecture 86 | 87 | * [Long Short Term Memory Network](https://www.mitpressjournals.org/doi/abs/10.1162/neco.1997.9.8.1735) (**Journals** 1997) 88 | 89 | * 【VGG16】[Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) (**arxiv** 2014) 90 | 91 | * [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/abs/1511.06581) (**arxiv** 2015) 92 | 93 | * 【STN】[Recurrent Spatial Transformer Networks](https://arxiv.org/abs/1509.05329) (**arxiv** 2015) 94 | 95 | * 【FCN】[Fully Convolutional Networks for Semantic Segmentation](https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf) (**cv-foundation** 2015) 96 | 97 | * [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf) (**cv-foundation** 2015) 98 | 99 | * 【C3D】[Learning Spatiotemporal Features with 3D Convolutional Networks](http://ieeexplore.ieee.org/abstract/document/7410867/) (**ICCV** 2015) 100 | 101 | * 【ResNet】[Deep Residual Learning for Image Recognition](http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) (**openaccess.thecvf** 2016) 102 | 103 | * 【GCN】[Semi-supervised classification with graph convolutional networks](https://arxiv.org/pdf/1609.02907.pdf) (**ICLR** 2017) 104 | 105 | * [Non-local Neural Networks](https://arxiv.org/abs/1711.07971) (**arxiv** 2017) 106 | 107 | * 【SPN】[Learning Affinity via Spatial Propagation Networks](http://papers.nips.cc/paper/6750-learning-affinity-via-spatial-propagation-networks) (**NIPS** 2017) 108 | 109 | * 【Capsule】[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829) (**NIPS** 2017) 110 | 111 | * [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) (**arxiv** 2017) 112 | 113 | * [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://arxiv.org/abs/1711.05769) (**arxiv** 2017) 114 | 115 | * [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://arxiv.org/abs/1711.00199) (**arxiv** 2017) 116 | 117 | * 【PSMNet】[Pyramid Stereo Matching Network](https://arxiv.org/abs/1803.08669) (**CVPR** 2018) 118 | 119 | * [PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image](http://art-programmer.github.io/planenet/paper.pdf) (**CVPR** 2018) 120 | 121 | * [SBNet: Sparse Blocks Network for Fast Inference](https://arxiv.org/abs/1801.02108) (**arxiv** 2018) 122 | 123 | ## Neural Network Component 124 | 125 | * 【ReLu】[Rectified Linear Units Improve Restricted Boltzmann Machines](https://www.cs.toronto.edu/~hinton/absps/reluICML.pdf) (**ICML** 2010) 126 | 127 | * [Dropout: a simple way to prevent neural networks from overfitting](http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b43&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer) (**jmlr** 2014) 128 | 129 | * [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167) (**ICML** 2015) 130 | 131 | * 【center loss】[A Discriminative Feature Learning Approach for Deep Face Recognition](https://link.springer.com/chapter/10.1007/978-3-319-46478-7_31) (**ECCV** 2016) 132 | 133 | * [Group Normalization](https://arxiv.org/abs/1803.08494) (**arxiv** 2018) 134 | 135 | ## Optimizer 136 | 137 | * 【ASGD】[Acceleration of stochastic approximation by averaging](https://dl.acm.org/citation.cfm?id=131098) (**Journals** 1992) 138 | 139 | * 【Adagrad】[Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://jmlr.org/papers/v12/duchi11a.html) (**jmlr** 2011) 140 | 141 | * [ADADELTA: An Adaptive Learning Rate Method](https://arxiv.org/abs/1212.5701) (**arxiv** 2012) 142 | 143 | * 【RMSprop】[Generating Sequences With Recurrent Neural Networks](https://arxiv.org/abs/1308.0850) (**arxiv** 2013) 144 | 145 | * [Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980) (**ICLR** 2015) 146 | 147 | * [Learning to learn by gradient descent by gradient descent](https://arxiv.org/abs/1606.04474) (**NIPS** 2016) 148 | 149 | # Computer Vision 150 | 151 | ## Dataset 152 | 153 | * [ImageNet: A large-scale hierarchical image database](http://ieeexplore.ieee.org/abstract/document/5206848/) (**CVPR** 2009) 154 | 155 | * 【NYUV2】[Indoor segmentation and support inference from rgbd images](https://link.springer.com/chapter/10.1007%2F978-3-642-33715-4_54) (**ECCV** 2012) 156 | 157 | * 【KITTI】[Vision meets robotics: The KITTI dataset](http://journals.sagepub.com/doi/full/10.1177/0278364913491297) (**IJRR** 2013) 158 | 159 | * 【Daimler Urban Segmentation】[Efficient Multi-Cue Scene Segmentation](http://pdfs.semanticscholar.org/bb9b/45f4b97935a95272c409d212589bc2a9a0cc.pdf) (**GCPR** 2013) 160 | 161 | * 【Pascal Context】[The Role of Context for Object Detection and Semantic Segmentation in the Wild](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Mottaghi_The_Role_of_2014_CVPR_paper.pdf) (**CVPR** 2014) 162 | 163 | * 【Pascal VOC】[The Pascal Visual Object Classes Challenge: A Retrospective](https://link.springer.com/article/10.1007/s11263-014-0733-5) (**IJCV** 2014) 164 | 165 | * 【COCO】[Microsoft COCO: Common Objects in Context](https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48) (**ECCV** 2014) 166 | 167 | * 【ILSVRC】[ImageNet Large Scale Visual Recognition Challenge](https://link.springer.com/article/10.1007/s11263-015-0816-y) (**IJCV** 2015) 168 | 169 | * 【ModelNet40】[3D ShapeNets: A Deep Representation for Volumetric Shapes](https://pdfs.semanticscholar.org/3ed2/3386284a5639cb3e8baaecf496caa766e335.pdf) (**CVPR** 2015) Dataset is available at [\[website\]](http://modelnet.cs.princeton.edu/). 170 | 171 | * 【Cityscapes】[The Cityscapes Dataset for Semantic Urban Scene Understanding](http://openaccess.thecvf.com/content_cvpr_2016/papers/Cordts_The_Cityscapes_Dataset_CVPR_2016_paper.pdf) (**CVPR** 2016) 172 | 173 | * 【S3DIS】[3d semantic parsing of largescale indoor spaces](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Armeni_3D_Semantic_Parsing_CVPR_2016_paper.pdf) (**CVPR** 2016) 174 | 175 | * 【ScanNet】[ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://arxiv.org/pdf/1702.04405.pdf) (**CVPR workshop** 2018) 176 | 177 | ## 2D Object Detection 178 | 179 | ### Algorithm 180 | 181 | * 【R-CNN】[Rich feature hierarchies for accurate object detection and semantic segmentation](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf?spm=5176.100239.blogcont55892.8.pm8zm1&file=Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) (**CVPR** 2014) 182 | 183 | * [Fast R-CNN](http://openaccess.thecvf.com/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf) (**ICCV** 2015) 184 | 185 | * [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf) (**NIPS** 2015) 186 | 187 | * [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) (**ECCV** 2016) 188 | 189 | * [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://arxiv.org/abs/1605.06409) (**NIPS** 2016) 190 | 191 | * 【YOLO】[You Only Look Once: Unified, Real-Time Object Detection](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf) (**CVPR** 2016) 192 | 193 | * [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) (**openaccess.thecvf** 2017) 194 | 195 | * [FSSD: Feature Fusion Single Shot Multibox Detector](https://arxiv.org/abs/1712.00960) (**arxiv** 2017) 196 | 197 | * 【RFB-SSD】[Receptive Field Block Net for Accurate and Fast Object Detection](https://arxiv.org/abs/1711.07767) (**arxiv** 2017) 198 | 199 | * 【RefineDet】[Single-Shot Refinement Neural Network for Object Detection](https://arxiv.org/abs/1711.06897) (**arxiv** 2017) 200 | 201 | * [MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240) (**arxiv** 2017) 202 | 203 | * [Light-Head R-CNN: In Defense of Two-Stage Object Detector](https://arxiv.org/abs/1711.07264) (**arxiv** 2017) 204 | 205 | * 【RetinaNet / Focal Loss】[Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) (**ICCV** 2017) 206 | 207 | * [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf) (**??** 2018) 208 | 209 | ## 3D Object Detection 210 | 211 | ### Algorithm 212 | 213 | * [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://arxiv.org/abs/1711.10006) (**openaccess.thecvf** 2017) 214 | 215 | * 【Frustum PointNets】[Frustum PointNets for 3D Object Detection from RGB-D Data](https://arxiv.org/pdf/1711.08488.pdf) (**arxiv** 2017) 216 | 217 | ## 2D Segmentation 218 | 219 | ### Algorithm 220 | 221 | * 【U-Net】[U-net: Convolutional networks for biomedical image segmentation](http://www.cs.cmu.edu/~jeanoh/16-785/papers/ronnenberger-miccai2015-u-net.pdf) (**arxiv** 2015) 222 | 223 | * 【DeepMask】[Learning to Segment Object Candidates](https://arxiv.org/pdf/1506.06204.pdf) (**arxiv** 2015) 224 | 225 | * [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://arxiv.org/abs/1512.04412) (**CVPR** 2016) 226 | 227 | * [Mask R-CNN](https://arxiv.org/abs/1703.06870) (**ICCV** 2017) 228 | 229 | * 【W-Net】[W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://arxiv.org/pdf/1711.08506.pdf) (**arxiv** 2017) 230 | 231 | * 【RefineNet】[RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_RefineNet_Multi-Path_Refinement_CVPR_2017_paper.pdf) (**CVPR** 2017) 232 | 233 | * [Semantic Instance Segmentation with a Discriminative Loss Function](https://arxiv.org/abs/1708.02551) (**CVPR workshop** 2017) 234 | 235 | * [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://arxiv.org/abs/1711.09081) (**CVPR** 2018) 236 | 237 | * [Weakly Supervised Instance Segmentation using Class Peak Response](https://arxiv.org/pdf/1804.00880.pdf) (**CVPR** 2018) 238 | 239 | * 【Mask^X RCNN】[Learning to Segment Every Thing](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Learning_to_Segment_CVPR_2018_paper.pdf) (**CVPR** 2018) 240 | 241 | ## 3D Segmentation 242 | 243 | ### Algorithm 244 | 245 | * [Deep learning with sets and point clouds](https://arxiv.org/pdf/1611.04500.pdf) (**ICLR** 2017) 246 | 247 | * [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf) (**CVPR** 2017) 248 | 249 | * [3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues](https://arxiv.org/pdf/1711.11379.pdf) (**CVPR** 2017) 250 | 251 | * [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/abs/1706.02413) (**NIPS** 2017) 252 | 253 | * [Escape from cells: Deep kd-networks for the recognition of 3d point cloud models](http://openaccess.thecvf.com/content_ICCV_2017/papers/Klokov_Escape_From_Cells_ICCV_2017_paper.pdf) (**ICCV** 2017) 254 | 255 | * 【PointSIFT】[PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://arxiv.org/abs/1807.00652) (**arxiv** 2018) 256 | 257 | * 【Kd-network】[PointCNN](https://arxiv.org/abs/1801.07791) (**arxiv** 2018) 258 | 259 | * 【SO-Net】[SO-Net: Self-Organizing Network for Point Cloud Analysis](https://arxiv.org/pdf/1803.04249.pdf) (**CVPR** 2018) 260 | 261 | * 【SGPN】[SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://arxiv.org/pdf/1711.08588.pdf) (**CVPR** 2018) 262 | 263 | * 【DGCNN】[Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/pdf/1801.07829.pdf) 264 | 265 | ## 2D Pose 266 | 267 | ### Algorithm 268 | 269 | * 【open pose】[Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](http://openaccess.thecvf.com/content_cvpr_2017/papers/Cao_Realtime_Multi-Person_2D_CVPR_2017_paper.pdf) (**CVPR** 2017) 270 | 271 | * 【G-RMI】[Towards accurate multi-person pose estimation in the wild](http://openaccess.thecvf.com/content_cvpr_2017/papers/Papandreou_Towards_Accurate_Multi-Person_CVPR_2017_paper.pdf) (**CVPR** 2017) 272 | 273 | * [Joint Multi-Person Pose Estimation and Semantic Part Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Xia_Joint_Multi-Person_Pose_CVPR_2017_paper.pdf) (**CVPR** 2017) 274 | 275 | * [RMPE: Regional Multi-Person Pose Estimation](http://openaccess.thecvf.com/content_ICCV_2017/papers/Fang_RMPE_Regional_Multi-Person_ICCV_2017_paper.pdf) (**ICCV** 2017) 276 | 277 | * [Vnect: Real-time 3d human pose estimation with a single rgb camera](https://dl.acm.org/citation.cfm?id=3073596) (**SIGGRAPH** 2017) 278 | 279 | * 【CPN】[Cascaded Pyramid Network for Multi-Person Pose Estimation](https://arxiv.org/abs/1711.07319) (**CVPR** 2018) 280 | 281 | ## 3D Pose 282 | 283 | ### Algorithm 284 | 285 | * [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](http://openaccess.thecvf.com/content_cvpr_2017/papers/Pavlakos_Coarse-To-Fine_Volumetric_Prediction_CVPR_2017_paper.pdf) (**CVPR** 2017) 286 | 287 | ## Video 288 | 289 | ### Segmentation 290 | 291 | * [Learning to Segment Instances in Videos with Spatial Propagation Network](https://arxiv.org/abs/1709.04609) (**CVPR workshop 2017**) 292 | 293 | * [SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](http://openaccess.thecvf.com/content_ICCV_2017/papers/Cheng_SegFlow_Joint_Learning_ICCV_2017_paper.pdf) (**CVPR** 2017) 294 | 295 | * [Learning Features by Watching Objects Move](http://openaccess.thecvf.com/content_cvpr_2017/papers/Pathak_Learning_Features_by_CVPR_2017_paper.pdf) (**CVPR** 2017) 296 | 297 | ### Motion Representation 298 | 299 | * 【TVnet】[End-to-End Learning of Motion Representation for Video Understanding](https://arxiv.org/abs/1804.00413) (**CVPR** 2018) 300 | 301 | # Generative Model 302 | 303 | ## VAE (Variational Auto-Encoder) 304 | 305 | ### Models 306 | 307 | ### Applications 308 | 309 | * [Attribute2Image: Conditional Image Generation from Visual Attributes](https://link.springer.com/chapter/10.1007/978-3-319-46493-0_47) (**ECCV** 2016) 310 | 311 | ## GAN (Generative Adversarial Networks) 312 | 313 | ### Models 314 | 315 | * 【GAN】[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) (**NIPS** 2014) 316 | 317 | * 【CGAN】[Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784) (**arxiv** 2014) 318 | 319 | * 【AAE】[Adversarial Autoencoders](https://arxiv.org/abs/1511.05644) (**arxiv** 2015) 320 | 321 | * 【DCGAN】[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) (**arxiv** 2015) 322 | 323 | ### Applications 324 | 325 | # Reinforcement Learning 326 | 327 | ## Environment 328 | 329 | * [MuJoCo: A physics engine for model-based control](http://ieeexplore.ieee.org/abstract/document/6386109/?reload=true) (**International Conference on Intelligent Robots and Systems** 2012) 330 | 331 | * [OpenAI Gym](https://arxiv.org/abs/1606.01540) (**arxiv** 2016) 332 | 333 | * 【rllab】[Benchmarking Deep Reinforcement Learning for Continuous Control](https://arxiv.org/abs/1604.06778) (**jmlr** 2016) 334 | 335 | * [DeepMind Lab](https://arxiv.org/abs/1612.03801) (**arxiv** 2016) 336 | 337 | * [StarCraft II: A New Challenge for Reinforcement Learning](https://arxiv.org/abs/1708.04782) (**arxiv** 2017) 338 | 339 | * [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://arxiv.org/abs/1712.00600) (**arxiv** 2017) 340 | 341 | ## Algorithm 342 | 343 | * [Q-learning](https://link.springer.com/article/10.1007/BF00992698) (**Springer** 1992) 344 | 345 | * 【DQN】[Playing Atari with Deep Reinforcement Learning](https://arxiv.org/abs/1312.5602) (**NIPS workshop** 2013) 346 | 347 | * 【DPG】[Deterministic Policy Gradient Algorithms](https://hal.inria.fr/hal-00938992/) (**ICML** 2014) 348 | 349 | * 【TRPO】[Trust Region Policy Optimization](http://proceedings.mlr.press/v37/schulman15.pdf) (**ICML** 2015) 350 | 351 | * 【Double-DQN】[Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461) (**AAAI** 2016) 352 | 353 | * 【h-DQN】[Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://arxiv.org/abs/1604.06057) (**NIPS** 2016) 354 | 355 | * 【A3C】[Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783) (**ICML** 2016) 356 | 357 | * 【DDPG】[Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971) (**ICLR** 2016) 358 | 359 | * 【NAF】[Continuous Deep Q-Learning with Model-based Acceleration](https://arxiv.org/abs/1603.00748) (**arxiv** 2016) 360 | 361 | * 【ACER】[Sample Efficient Actor-Critic with Experience Replay](https://arxiv.org/abs/1611.01224) (**arxiv** 2016) 362 | 363 | * 【GAIL】[Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476) (**NIPS** 2016) 364 | 365 | * [Neural Episodic Control](https://arxiv.org/abs/1703.01988) (**arxiv** 2017) 366 | 367 | * [Q-PROP: SAMPLE-EFFICIENT POLICY GRADIENT WITH AN OFF-POLICY CRITIC](https://arxiv.org/abs/1611.02247) (**ICLR** 2017) 368 | 369 | * 【PPO】[Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) (**arxiv** 2017) 370 | 371 | * [Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/abs/1707.02286) (**arxiv** 2017) 372 | 373 | * 【ACKTR】[Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation](https://arxiv.org/abs/1708.05144) (**NIPS** 2017) 374 | 375 | * 【HER】[Hindsight Experience Replay](https://arxiv.org/abs/1707.01495) (**NIPS** 2017) 376 | 377 | * [Noisy Networks for Exploration](https://arxiv.org/abs/1706.10295) (**ICLR** 2018) 378 | 379 | ## RL in Games 380 | 381 | * [Control of Memory, Active Perception, and Action in Minecraft](https://arxiv.org/abs/1605.09128) (**arxiv** 2016) 382 | 383 | ## Distributional RL 384 | 385 | * [A Distributional Perspective on Reinforcement Learning](https://arxiv.org/abs/1707.06887) (**arxiv** 2017) 386 | 387 | ## Curiosity-Driven RL 388 | 389 | * [Curiosity-driven Exploration by Self-supervised Prediction](https://arxiv.org/pdf/1705.05363.pdf) (**ICML** 2017) 390 | 391 | * [Intrinsically motivated model learning for developing curious robots](http://www.cs.utexas.edu/users/pstone/Papers/bib2html-links/AIJ15-Hester.pdf) (**Artificial Intelligence** 2017) 392 | 393 | * [Computational Theories of Curiosity-Driven Learning](https://arxiv.org/pdf/1802.10546.pdf) (**arxiv** 2018) 394 | 395 | * [Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation](https://arxiv.org/pdf/1802.07461.pdf) (**arxiv** 2018) 396 | 397 | # Transfer Learning & Meta Learning 398 | 399 | ## Algorithm or Model 400 | 401 | * 【MAML】[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400) (**arxiv** 2017) 402 | 403 | * [OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING](https://openreview.net/forum?id=rJY0-Kcll¬eId=ryq49XyLg) (**ICLR** 2017) 404 | 405 | * 【SNAIL】[A Simple Neural Attentive Meta-Learner](https://openreview.net/forum?id=B1DmUzWAW¬eId=B1DmUzWAW) (**ICLR** 2018) 406 | 407 | ## Zero Shot Learning 408 | 409 | * [ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems](https://web.stanford.edu/~yukez/papers/isrr2017.pdf) (**isrr** 2017) 410 | 411 | * [Zero-Shot Object Detection](https://arxiv.org/abs/1804.04340) (**arxiv** 2018) 412 | 413 | * [Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs](https://arxiv.org/pdf/1803.08035.pdf) (**CVPR** 2018) 414 | 415 | # Robot 416 | 417 | ## Hardware 418 | 419 | ## Grasping 420 | 421 | ### Dataset 422 | 423 | * [Deep Grasp: Detection and Localization of Grasps with Deep Neural Networks](https://arxiv.org/abs/1802.00520) (**arxiv** 2018) 424 | 425 | * [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://arxiv.org/abs/1803.11469) (**arxiv** 2018) 426 | 427 | ### Grasping with RL 428 | 429 | ### Grasping Unknown Objects 430 | 431 | * [Ranking the good points: A comprehensive method for humanoid robots to grasp unknown objects](http://poeticonpp.csri-web.org:8989/PoeticonPlus/publications/1342_Gori_etal2013.pdf) (**International Conference on Advanced Robotics** 2013) 432 | 433 | * [Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects](http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/ECCV_2014/papers/8693/86930659.pdf) (**ECCV** 2014) 434 | 435 | * [Pick and Place Without Geometric Object Models](https://arxiv.org/pdf/1707.05615.pdf) (**ICRA** 2018) 436 | 437 | ### Grasping in Cluttered Environment 438 | 439 | * [Real-Time 3D Segmentation of Cluttered Scenes for Robot Grasping](https://pub.uni-bielefeld.de/publication/2530701) (**ICHR** 2012) 440 | 441 | * 【GPD】[High precision grasp pose detection in dense clutter](https://arxiv.org/pdf/1603.01564.pdf) (**IROS** 2016) 442 | 443 | * [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](http://vision.princeton.edu/projects/2017/arc/paper.pdf) (**arxiv** 2017) 444 | 445 | ### Grasping via Segmentation 446 | 447 | * [Grasping novel objects with depth segmentation](http://www.robotics.stanford.edu/~ang/papers/iros10-GraspingWithDepthSegmentation.pdf) (**IROS** 2010) 448 | 449 | * [3D scene segmentation for autonomous robot grasping](https://www.researchgate.net/publication/261353757_3D_scene_segmentation_for_autonomous_robot_grasping) (**IROS** 2012) 450 | 451 | ### Grasping Points Selection 452 | 453 | * [GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming](http://rll.berkeley.edu/~sachin/papers/Mahler-ICRA2015.pdf) (**ICRA** 2015) 454 | 455 | * [Using Geometry to Detect Grasp Poses in 3D Point Clouds](http://www.ccs.neu.edu/home/atp/publications/grasp_poses_isrr2015.pdf) (**ISRR** 2015) 456 | 457 | * [Dex-Net 1.0: A Cloud-Based Network of 3D Objects for Robust Grasp Planning Using a Multi-Armed Bandit Model with Correlated Rewards](http://goldberg.berkeley.edu/pubs/icra16-submitted-Dex-Net.pdf) (**ICRA** 2016) 458 | 459 | * [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://arxiv.org/abs/1703.09312) (**arxiv** 2017) 460 | 461 | * [Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning](https://arxiv.org/abs/1709.06670) (**arxiv** 2017) 462 | 463 | ## Machine Vision 464 | 465 | ## Active Perception 466 | 467 | * [Active Perception: Interactive Manipulation for Improving Object Detection](https://pdfs.semanticscholar.org/3835/6590cdd2093377717db243a98e62759d9b2a.pdf) (**Standford University Journal** 2018) 468 | 469 | * [Learning Instance Segmentation by Interaction](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w40/Pathak_Learning_Instance_Segmentation_CVPR_2018_paper.pdf) (**CVPR** 2018) 470 | 471 | ### Motion Prediction 472 | 473 | * 【SE3-Net】[SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks](https://arxiv.org/pdf/1606.02378.pdf) (**ICRA** 2017) 474 | 475 | ## Interactive Perception 476 | 477 | * [Better Vision through Manipulation](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.120.9729&rep=rep1&type=pdf) (**Adaptive Behavior** 2003) 478 | 479 | * [Interactive Perception: Closing the Gap Between Action and Perception](http://www.dubikatz.com/docs/papers/Interactive%20Perception%20Closing%20the%20Gap%20Between%20Action%20and%20Perception.pdf) (**ICRA** 2007) 480 | 481 | * [BIRTH OF THE OBJECT: DETECTION OF OBJECTNESS AND EXTRACTION OF OBJECT SHAPE THROUGH OBJECT–ACTION COMPLEXES](https://www.researchgate.net/profile/Danica_Kragic/publication/220065640_Birth_of_the_Object_Detection_of_Objectness_and_Extraction_of_Object_Shape_through_Object-Action_complexes/links/0deec52b935fedd8a8000000.pdf) (**International Journal of Humanoid Robotics** 2008) 482 | 483 | * [Interactive Segmentation for Manipulation in Unstructured Environments](http://www.redaktion.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/2009-icra.pdf) (**ICRA** 2009) 484 | 485 | * [Generating Object Hypotheses in Natural Scenes through Human-Robot Interaction](http://www.diva-portal.org/smash/get/diva2:448466/FULLTEXT01.pdf) (**IROS** 2011) 486 | 487 | * [Segmentation and learning of unknown objects through physical interaction](http://h2t.anthropomatik.kit.edu/pdf/Schiebener2011.pdf) (**IEEE/RAS Int. Conf. on Humanoid Robots (Humanoids)** 2011) 488 | 489 | * [Clearing a Pile of Unknown Objects using Interactive Perception](https://www.ri.cmu.edu/pub_files/2012/11/article_dov2.pdf) (**??** 2012) 490 | 491 | * [Segmentation of Cluttered Scenes through Interactive Perception](https://www.researchgate.net/profile/Karol_Hausman/publication/273143501_Segmentation_of_Textured_and_Textureless_Objects_through_Interactive_Perception/links/54fa1e690cf23e66f0311639.pdf) (**ICRA** 2012) 492 | 493 | * [Interactive singulation of objects from a pile](https://rse-lab.cs.washington.edu/papers/object-singulation-icra-12.pdf) (**ICRA** 2012) 494 | 495 | * [Tracking-based Interactive Segmentation of Textureless Objects](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.948&rep=rep1&type=pdf) (**ICRA** 2013) 496 | 497 | * [Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments](https://www.ias.informatik.tu-darmstadt.de/uploads/Publications/hoof2014probabilistic.pdf) (**Robotics, IEEE Transactions** 2014) 498 | 499 | * [Interactive perception: Leveraging action in perception and perception in action](https://arxiv.org/pdf/1604.03670.pdf) (**arxiv** 2016) 500 | 501 | * [Segmenting objects through an autonomous agnostic exploration conducted by a robot](https://www.computer.org/csdl/proceedings/irc/2017/6724/00/07926551.pdf) (**IRC** 2017) 502 | 503 | * [Learning Instance Segmentation by Interaction](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w40/Pathak_Learning_Instance_Segmentation_CVPR_2018_paper.pdf) (**CVPR** 2018) 504 | 505 | 506 | 507 | 508 | 509 | --------------------------------------------------------------------------------