├── .gitattributes ├── LICENSE └── README.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Weakly-supervised Object Localization 2 | 3 | [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)![GitHub stars](https://img.shields.io/github/stars/gyguo/awesome-weakly-supervised-object-localization?color=yellow) ![GitHub forks](https://img.shields.io/github/forks/gyguo/awesome-weakly-supervised-object-localization?color=green&label=Fork) 4 | 5 | ------ 6 | 7 | **Contact gyguo95@gmail.com if any paper is missed!** 8 | 9 | ------ 10 | 11 | # Table of Contents 12 | - [1. Paper List](#1-paper-list) 13 | - [2. Performance](#2-performance) 14 | * [2.1. Top1/5 results on CUB-200-2011](#21-results-on-cub-200-2011) 15 | * [2.2. Top1/5 results on ImageNet](#22-results-on-imagenet) 16 | - [3. Dataset](#3-dataset) 17 | * [CUB-200-2011](#cub-200-2011) 18 | * [ImageNet](#imagenet) 19 | - [4. Awesome-list of Weakly-supervised Learning from Our Team](#4-awesome-list-of-weakly-supervised-learning-from-our-team) 20 | 21 | # 1. Paper List 22 | ## 2025 23 | - ***2025TPAMI*** CLIP-Driven Transformer for Weakly Supervised Object Localization 24 | - ***2025TNNLS*** Weakly Supervised Object Localization With Progressive Activation Diffusion 25 | - ***2025TIP*** Local Cross-Patch Activation From Multi-Direction for Weakly Supervised Object Localization 26 | - ***2024Arxiv*** TeD-Loc: Text Distillation for Weakly Supervised Object Localization 27 | - ***2024Arxiv*** A Realistic Protocol for Evaluation of Weakly Supervised Object Localization 28 | 29 | ## 2024 30 | - ***2024ECCV*** Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization 31 | - ***2024IJCAI*** A Consistency and Integration Model with Adaptive Thresholds for Weakly Supervised Object Localization 32 | - ***2024CVPR*** CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective 33 | - ***2024TPAMI*** Boosting Weakly Supervised Object Localization and Segmentation With Domain Adaption 34 | - ***2024PR*** Discovering an inference recipe for weakly-supervised object localization 35 | - ***2024TNNLS*** Adaptive Zone Learning for Weakly Supervised Object Localization 36 | - ***2024PR*** Semantic-Constraint Matching for transformer-based weakly supervised object localization 37 | - ***2024TMM*** Token Masking Transformer for Weakly Supervised Object Localization 38 | 39 | ## 2023 40 | - ***2023TPAMI*** Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets 41 | - ***2023ACM MM*** LocLoc: Low-level Cues and Local-area Guides for Weakly Supervised Object Localization 42 | - ***2023ACM MM*** Rethinking the Localization in Weakly Supervised Object Localization 43 | - ***2023ICCV*** Generative Prompt Model for Weakly Supervised Object Localization 44 | - ***2023PR*** Weakly supervised foreground learning for weakly supervised localization and detection 45 | 46 | ## 2022 47 | - ***2022CVPR*** C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation 48 | - ***2022ECCV*** Bagging regional classification activation maps for weakly supervised object localization 49 | - **CREAM:** ***2022CVPR*** CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping 50 | - **DA-WSOL:** ***2022CVPR*** Weakly Supervised Object Localization as Domain Adaption 51 | - **AlignMix:** ***2022CVPR*** AlignMix: Improving representation by interpolating aligned features 52 | - **ViTOL:** ***2022CVPRW*** ViTOL: Vision Transformer for Weakly Supervised Object Localization 53 | - ***2022TNNLS*** Diverse Complementary Part Mining for Weakly Supervised Object Localization 54 | - ***2022TNNLS*** Generalized Weakly Supervised Object Localization 55 | - ***2022PR*** Gradient-based refined class activation map for weakly supervised object localization 56 | - ***2022TMM*** Dual-Gradients Localization Framework With Skip-Layer Connections for Weakly Supervised Object Localization 57 | - ***2022ICMR*** FreqCAM: Frequent Class Activation Map for Weakly Supervised Object Localization 58 | - **SCM:2022ECCV** Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration 59 | - ***2022arxiv*** Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization 60 | 61 | ## 2021 62 | 63 | - **SLT-Net: 2021CVPR**: Strengthen Learning Tolerance for Weakly Supervised Object Localization 64 | - **TS-CAM:** ***2021ICCV*** TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization 65 | - ***2021TIP*** Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization 66 | - ***2021TIP*** LayerCAM: Exploring Hierarchical Class Activation Maps for Localization 67 | - ***2021PR*** Region-based dropout with attention prior for weakly supervised object localization 68 | - ***2021arxiv*** Background-aware Classification Activation Map for Weakly Supervised Object Localization 69 | - ***2021arxiv*** MinMaxCAM Improving object coverage for CAM-based Weakly Supervised Object Localization 70 | - ***2021arxiv*** Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection 71 | 72 | ## 2020 73 | 74 | - **PSOL:** ***2020CVPR*** Rethinking the Route Towards Weakly Supervised Object Localization 75 | - ***2020CVPR*** Evaluating Weakly Supervised Object Localization Methods Right 76 | - **MEIL:** ***2020CVPR*** Erasing Integrated Learning A Simple yet Effective Approach for Weakly Supervised Object Localization 77 | - **GC-Net:** ***2020ECCV*** Geometry Constrained Weakly Supervised Object Localization 78 | - ***I2C:*** ***2020ECCV*** Inter-Image Communication for Weakly Supervised Localization 79 | - ***2020ECCV*** Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization 80 | - ***2020ICPR*** Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization 81 | - ***2020arxiv*** Rethinking Localization Map Towards Accurate Object Perception with Self-Enhancement Maps 82 | 83 | ## 2019 84 | 85 | - **ADL:** ***2019CVPR*** Attention-based Dropout Layer for Weakly Supervised Object Localization 86 | - **DANet:** ***2019ICCV*** DANet: Divergent Activation for Weakly Supervised Object Localization 87 | - **CutMix:** ***2019ICCV*** CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features 88 | - ***2019ICLR*** Marginalized average attentional network for weakly-supervised learning 89 | - ***2019arxiv*** Dual-attention Focused Module for Weakly Supervised Object Localization 90 | - ***2019arxiv*** Weakly Supervised Localization Using Background Images 91 | - ***2019arxiv*** Weakly Supervised Object Localization with Inter-Intra Regulated CAMs 92 | 93 | ## 2018 94 | 95 | - **ACoL:** ***2018CVPR*** Adversarial Complementary Learning for Weakly Supervised Object Localization 96 | - **SPG:** ***2018ECCV*** Self-produced Guidance for Weakly-supervised Object Localization 97 | 98 | ## 2017 99 | 100 | - **Grad-CAM:** ***2017ICCV*** Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization 101 | - **HaS:** ***2017ICCV*** Hide-and-Seek Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization 102 | 103 | ## 2016 104 | 105 | - **CAM:** ***2016CVPR*** Learning Deep Features for Discriminative Localization 106 | 107 | 108 | # 2. Performance 109 | **Performance will no be updated anymore** 110 | - **Bac. C:** backbone for classification 111 | - **Bac. L:** backbone for localization, **does not exist** for methods use a single network for classification and localization. 112 | - **Top-1/Top-5 CLS:** is correct if the Top-1/Top-5 predict categories contain the correct label. 113 | - **GT-known Loc** is correct when the intersection over union (IoU) between the ground-truth and the prediction is larger than 0.5 and does not consider whether the predicted category is correct. 114 | - **Top-1/Top-5 Loc** is correct when Top-1/Top-5 CLS and GT-Known LOC are both correct. 115 | - **"-"** indicates not exist. **"?"** indicates the corresponding item is not mentioned in the paper. 116 | 117 | ## 2.1. Results on CUB-200-2011 118 | 119 | ### Transformer 120 | 121 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 122 | |:-------- |:---------:|:---------------:|:--------:|:-----------:|:--------:|:-----------:| 123 | | GenPromp | 2023CVPR | EfficientNet-B7 | - | 87.0/96.1 | 98.0 | -/- | 124 | | WEND | 2023ACMMM | EfficientNet-B7 | ResNet50 | 83.77/93.84 | 95.78 | -/- | 125 | | SCM | 2022ECCV | Deit-S | - | 76.4/91.6 | 96.6 | 78.5/94.5 | 126 | | TS-CAM | 2021ICCV | Deit-S | - | 71.3/83.8 | 87.7 | -/- | 127 | 128 | ### VGG 129 | 130 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 131 | |:------- |:--------:|:-----:|:-----:|:-----------:|:--------:|:-----------:| 132 | | CREAM | 2022CVPR | VGG16 | - | 70.4/85.7 | 91.0 | -/- | 133 | | SLT-Net | 2021CVPR | VGG16 | VGG16 | 67.8/- | 87.6 | 76.6/- | 134 | | PSOL | 2020CVPR | VGG16 | VGG16 | 66.3/84.1 | - | -/- | 135 | | GC-Net | 2020ECCV | VGG16 | VGG16 | 63.2/75.5 | 81.1 | 76.8/92.3 | 136 | | MEIL | 2020CVPR | VGG16 | - | 57.5/- | 73.8 | 74.8/- | 137 | | DANet | 2019ICCV | VGG16 | - | 52.5/62.0 | 67.7 | 75.4/92.3 | 138 | | CutMix | 2019ICCV | VGG16 | - | 52.5/- | - | - | 139 | | ADL | 2019CVPR | VGG16 | - | 52.4/- | 75.4 | 65.3/- | 140 | | CAM | 2016CVPR | VGG16 | - | 44.2/52.2 | 56.0 | 76.6/92.5 | 141 | | SPG | 2018ECCV | VGG16 | - | 48.9/57.9 | 58.9 | 75.5/92.1 | 142 | 143 | ### InceptionV3 144 | 145 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 146 | |:------- |:--------:|:-----------:|:-----------:|:-----------:|:--------:|:-----------:| 147 | | CREAM | 2022CVPR | InceptionV3 | - | 71.8/86.4 | 90.4 | -/- | 148 | | SLT-Net | 2021CVPR | InceptionV3 | VGG16 | 66.1/- | 86.5 | 76.4/- | 149 | | PSOL | 2020CVPR | InceptionV3 | InceptionV3 | 65.5/83.4 | - | -/- | 150 | | I2C | 2020ECCV | InceptionV3 | | 56.0/68.3 | 72.6 | -/- | 151 | | DANet | 2019ICCV | InceptionV3 | - | 49.5/60.5 | 67.0 | 71.2/90.6 | 152 | | ADL | 2019CVPR | InceptionV3 | - | 53.0/- | - | 74.6/- | 153 | | SPG | 2018ECCV | InceptionV3 | - | 46.6/57.7 | - | - | 154 | 155 | ### Others 156 | 157 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 158 | |:------- |:--------:|:-------------:|:-----:|:-----------:|:--------:|:-----------:| 159 | | | | **ResNet50** | | | | | 160 | | DA-WSOL | 2022CVPR | ResNet50 | - | 66.8/- | 82.3 | -/- | 161 | | CutMix | 2019ICCV | ResNet50 | - | 54.81/- | - | -/- | 162 | | | | **GoogleNet** | | | | | 163 | | CAM | 2016CVPR | GoogleNet | - | 41.1/50.7 | - | 73.8/91.5 | 164 | 165 | ## 2.2. Results on ImageNet 166 | 167 | ### Transformer 168 | 169 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 170 | |:-------- |:---------:|:---------------:|:-----:|:-----------:|:--------:|:-----------:| 171 | | GenPromp | 2023ICCV | EfficientNet-B7 | - | 65.2/73.4 | 75.0 | -/- | 172 | | ViTOL | 2022CVPRW | DeiT-B | - | 58.6/- | 72.5 | 77.1/- | 173 | | SCM | 2022ECCN | Deit-S | - | 56.1/66/4 | 68.8 | 76.7/93.0 | 174 | | TS-CAM | 2021ICCV | Deit-S | - | 53.4/64.3 | 67.6 | -/- | 175 | 176 | ### VGG 177 | 178 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 179 | |:------- |:--------:|:-----:|:-----------:|:-----------:|:--------:|:-----------:| 180 | | CREAM | 2022CVPR | VGG16 | - | 52.4/64.2 | 68.3 | -/- | 181 | | SLT-Net | 2021CVPR | VGG16 | InceptionV3 | 51.2/62.4 | 67.2 | 72.4/- | 182 | | PSOL | 2020CVPR | VGG16 | VGG16 | 50.9/60.9 | 64.0 | -/- | 183 | | I2C | 2020ECCV | VGG16 | - | 47.4/58.5 | 63.9 | 69.4/89.3 | 184 | | MEIL | 2020CVPR | VGG16 | - | 46.8/- | - | 70.3/- | 185 | | ADL | 2019CVPR | VGG16 | - | 44.9/- | - | 69.5/- | 186 | | CAM | 2016CVPR | VGG16 | - | 42.8/54.9 | 59.0 | 68.8/88.6 | 187 | 188 | ### InceptionV3 189 | 190 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 191 | |:------- |:--------:|:-----------:|:-----------:|:-----------:|:--------:|:-----------:| 192 | | CREAM | 2022CVPR | InceptionV3 | - | 56.1/66.2 | 69.0 | -/- | 193 | | SLT-Net | 2021CVPR | InceptionV3 | InceptionV3 | 55.7/65.4 | 67.6 | 78.1/- | 194 | | PSOL | 2020CVPR | InceptionV3 | InceptionV3 | 54.8/63.3 | 65.2 | -/- | 195 | | I2C | 2020ECCV | InceptionV3 | - | 53.1/64.1 | 68.5 | 73.3/91.6 | 196 | | GC-Net | 2020ECCV | InceptionV3 | InceptionV3 | 49.1/58.1 | - | 77.4/93.6 | 197 | | MEIL | 2020CVPR | InceptionV3 | - | 49.5/- | - | 73.3/- | 198 | | ADL | 2019CVPR | InceptionV3 | - | 48.7/- | - | 72.8/- | 199 | | SPG | 2018ECCV | InceptionV3 | - | 48.6/60.0 | 64.7 | | 200 | | CAM | 2016CVPR | InceptionV3 | - | 46.3/58.2 | 62.7 | 73.3/91.8 | 201 | 202 | ### Others 203 | | Method | Pub. | Bac.C | Bac.L | Top-1/5 Loc | GT-Known | Top-1/5 Cls | 204 | |:------- |:--------:|:-------------:|:-----:|:-----------:|:--------:|:-----------:| 205 | | | | **ResNet50** | | | | | 206 | | DA-WSOL | 2022CVPR | ResNet50 | - | 54.9/- | 70.2 | -/- | 207 | | CutMix | 2019ICCV | ResNet50 | - | 47.25/- | - | 78.6/94.1 | 208 | | | | **GoogleNet** | | | | | 209 | | CAM | 2016CVPR | GoogleNet | - | 41.1/50.7 | - | 73.8/91.5 | 210 | 211 | 212 | # 3. Dataset 213 | 214 | ## CUB-200-2011 215 | 216 | ``` 217 | @article{wah2011caltech, 218 | title={The caltech-ucsd birds-200-2011 dataset}, 219 | author={Wah, Catherine and Branson, Steve and Welinder, Peter and Perona, Pietro and Belongie, Serge}, 220 | year={2011}, 221 | publisher={California Institute of Technology} 222 | } 223 | ``` 224 | ## ImageNet 225 | 226 | ``` 227 | @inproceedings{deng2009imagenet, 228 | title={Imagenet: A large-scale hierarchical image database}, 229 | author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, 230 | booktitle={2009 IEEE conference on computer vision and pattern recognition}, 231 | pages={248--255}, 232 | year={2009}, 233 | organization={Ieee} 234 | } 235 | ``` 236 | 237 | --- 238 | # 4. Awesome-list of Weakly-supervised Learning from Our Team 239 | - [Awesome Weakly-supervised Semantic Segmentation](https://github.com/gyguo/awesome-weakly-supervised-semantic-segmentation) 240 | - [Awesome Weakly-supervised Action Localization](https://github.com/VividLe/awesome-weakly-supervised-action-localization) 241 | - [Awesome Weakly-supervised Object Localization](https://github.com/gyguo/awesome-weakly-supervised-object-localization) 242 | --------------------------------------------------------------------------------