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Awesome Weakly-supervised Object Localization

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Table of Contents

Contact [email protected] if any paper is missed!

1. Paper List

2024

  • 2024ECCV Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization
  • 2024IJCAI A Consistency and Integration Model with Adaptive Thresholds for Weakly Supervised Object Localization
  • 2024CVPR CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
  • 2024TPAMI Boosting Weakly Supervised Object Localization and Segmentation With Domain Adaption
  • 2024PR Discovering an inference recipe for weakly-supervised object localization
  • 2024TNNLS Adaptive Zone Learning for Weakly Supervised Object Localization
  • 2024PR Semantic-Constraint Matching for transformer-based weakly supervised object localization

2023

  • 2023TPAMI Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
  • 2023ACM MM LocLoc: Low-level Cues and Local-area Guides for Weakly Supervised Object Localization
  • WEND: 2023ACM MM Rethinking the Localization in Weakly Supervised Object Localization
  • GenPromp: 2023ICCV Generative Prompt Model for Weakly Supervised Object Localization
  • 2023PR Weakly supervised foreground learning for weakly supervised localization and detection

2022

  • 2022CVPR C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
  • 2022ECCV Bagging regional classification activation maps for weakly supervised object localization
  • CREAM: 2022CVPR CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
  • DA-WSOL: 2022CVPR Weakly Supervised Object Localization as Domain Adaption
  • AlignMix: 2022CVPR AlignMix: Improving representation by interpolating aligned features
  • ViTOL: 2022CVPRW ViTOL: Vision Transformer for Weakly Supervised Object Localization
  • 2022TNNLS Diverse Complementary Part Mining for Weakly Supervised Object Localization
  • 2022TNNLS Generalized Weakly Supervised Object Localization
  • 2022PR Gradient-based refined class activation map for weakly supervised object localization
  • 2022TMM Dual-Gradients Localization Framework With Skip-Layer Connections for Weakly Supervised Object Localization
  • 2022ICMR FreqCAM: Frequent Class Activation Map for Weakly Supervised Object Localization
  • SCM:2022ECCV Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
  • 2022arxiv Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization

2021

  • SLT-Net: 2021CVPR: Strengthen Learning Tolerance for Weakly Supervised Object Localization
  • TS-CAM: 2021ICCV TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
  • 2021TIP Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization
  • 2021TIP LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
  • 2021PR Region-based dropout with attention prior for weakly supervised object localization
  • 2021arxiv Background-aware Classification Activation Map for Weakly Supervised Object Localization
  • 2021arxiv MinMaxCAM Improving object coverage for CAM-based Weakly Supervised Object Localization
  • 2021arxiv Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection

2020

  • PSOL: 2020CVPR Rethinking the Route Towards Weakly Supervised Object Localization
  • 2020CVPR Evaluating Weakly Supervised Object Localization Methods Right
  • MEIL: 2020CVPR Erasing Integrated Learning A Simple yet Effective Approach for Weakly Supervised Object Localization
  • GC-Net: 2020ECCV Geometry Constrained Weakly Supervised Object Localization
  • I2C: 2020ECCV Inter-Image Communication for Weakly Supervised Localization
  • 2020ECCV Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
  • 2020ICPR Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
  • 2020arxiv Rethinking Localization Map Towards Accurate Object Perception with Self-Enhancement Maps

2019

  • ADL: 2019CVPR Attention-based Dropout Layer for Weakly Supervised Object Localization
  • DANet: 2019ICCV DANet: Divergent Activation for Weakly Supervised Object Localization
  • CutMix: 2019ICCV CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
  • 2019ICLR Marginalized average attentional network for weakly-supervised learning
  • 2019arxiv Dual-attention Focused Module for Weakly Supervised Object Localization
  • 2019arxiv Weakly Supervised Localization Using Background Images
  • 2019arxiv Weakly Supervised Object Localization with Inter-Intra Regulated CAMs

2018

  • ACoL: 2018CVPR Adversarial Complementary Learning for Weakly Supervised Object Localization
  • SPG: 2018ECCV Self-produced Guidance for Weakly-supervised Object Localization

2017

  • Grad-CAM: 2017ICCV Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
  • HaS: 2017ICCV Hide-and-Seek Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization

2016

  • CAM: 2016CVPR Learning Deep Features for Discriminative Localization

2. Performance

Performance will no be updated anymore

  • Bac. C: backbone for classification
  • Bac. L: backbone for localization, does not exist for methods use a single network for classification and localization.
  • Top-1/Top-5 CLS: is correct if the Top-1/Top-5 predict categories contain the correct label.
  • 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.
  • Top-1/Top-5 Loc is correct when Top-1/Top-5 CLS and GT-Known LOC are both correct.
  • "-" indicates not exist. "?" indicates the corresponding item is not mentioned in the paper.

2.1. Results on CUB-200-2011

Transformer

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
GenPromp 2023CVPR EfficientNet-B7 - 87.0/96.1 98.0 -/-
WEND 2023ACMMM EfficientNet-B7 ResNet50 83.77/93.84 95.78 -/-
SCM 2022ECCV Deit-S - 76.4/91.6 96.6 78.5/94.5
TS-CAM 2021ICCV Deit-S - 71.3/83.8 87.7 -/-

VGG

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
CREAM 2022CVPR VGG16 - 70.4/85.7 91.0 -/-
SLT-Net 2021CVPR VGG16 VGG16 67.8/- 87.6 76.6/-
PSOL 2020CVPR VGG16 VGG16 66.3/84.1 - -/-
GC-Net 2020ECCV VGG16 VGG16 63.2/75.5 81.1 76.8/92.3
MEIL 2020CVPR VGG16 - 57.5/- 73.8 74.8/-
DANet 2019ICCV VGG16 - 52.5/62.0 67.7 75.4/92.3
CutMix 2019ICCV VGG16 - 52.5/- - -
ADL 2019CVPR VGG16 - 52.4/- 75.4 65.3/-
CAM 2016CVPR VGG16 - 44.2/52.2 56.0 76.6/92.5
SPG 2018ECCV VGG16 - 48.9/57.9 58.9 75.5/92.1

InceptionV3

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
CREAM 2022CVPR InceptionV3 - 71.8/86.4 90.4 -/-
SLT-Net 2021CVPR InceptionV3 VGG16 66.1/- 86.5 76.4/-
PSOL 2020CVPR InceptionV3 InceptionV3 65.5/83.4 - -/-
I2C 2020ECCV InceptionV3 56.0/68.3 72.6 -/-
DANet 2019ICCV InceptionV3 - 49.5/60.5 67.0 71.2/90.6
ADL 2019CVPR InceptionV3 - 53.0/- - 74.6/-
SPG 2018ECCV InceptionV3 - 46.6/57.7 - -

Others

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
ResNet50
DA-WSOL 2022CVPR ResNet50 - 66.8/- 82.3 -/-
CutMix 2019ICCV ResNet50 - 54.81/- - -/-
GoogleNet
CAM 2016CVPR GoogleNet - 41.1/50.7 - 73.8/91.5

2.2. Results on ImageNet

Transformer

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
GenPromp 2023ICCV EfficientNet-B7 - 65.2/73.4 75.0 -/-
ViTOL 2022CVPRW DeiT-B - 58.6/- 72.5 77.1/-
SCM 2022ECCN Deit-S - 56.1/66/4 68.8 76.7/93.0
TS-CAM 2021ICCV Deit-S - 53.4/64.3 67.6 -/-

VGG

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
CREAM 2022CVPR VGG16 - 52.4/64.2 68.3 -/-
SLT-Net 2021CVPR VGG16 InceptionV3 51.2/62.4 67.2 72.4/-
PSOL 2020CVPR VGG16 VGG16 50.9/60.9 64.0 -/-
I2C 2020ECCV VGG16 - 47.4/58.5 63.9 69.4/89.3
MEIL 2020CVPR VGG16 - 46.8/- - 70.3/-
ADL 2019CVPR VGG16 - 44.9/- - 69.5/-
CAM 2016CVPR VGG16 - 42.8/54.9 59.0 68.8/88.6

InceptionV3

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
CREAM 2022CVPR InceptionV3 - 56.1/66.2 69.0 -/-
SLT-Net 2021CVPR InceptionV3 InceptionV3 55.7/65.4 67.6 78.1/-
PSOL 2020CVPR InceptionV3 InceptionV3 54.8/63.3 65.2 -/-
I2C 2020ECCV InceptionV3 - 53.1/64.1 68.5 73.3/91.6
GC-Net 2020ECCV InceptionV3 InceptionV3 49.1/58.1 - 77.4/93.6
MEIL 2020CVPR InceptionV3 - 49.5/- - 73.3/-
ADL 2019CVPR InceptionV3 - 48.7/- - 72.8/-
SPG 2018ECCV InceptionV3 - 48.6/60.0 64.7
CAM 2016CVPR InceptionV3 - 46.3/58.2 62.7 73.3/91.8

Others

Method Pub. Bac.C Bac.L Top-1/5 Loc GT-Known Top-1/5 Cls
ResNet50
DA-WSOL 2022CVPR ResNet50 - 54.9/- 70.2 -/-
CutMix 2019ICCV ResNet50 - 47.25/- - 78.6/94.1
GoogleNet
CAM 2016CVPR GoogleNet - 41.1/50.7 - 73.8/91.5

3. Dataset

CUB-200-2011

@article{wah2011caltech,
 title={The caltech-ucsd birds-200-2011 dataset},
 author={Wah, Catherine and Branson, Steve and Welinder, Peter and Perona, Pietro and Belongie, Serge},
 year={2011},
 publisher={California Institute of Technology}
}

ImageNet

@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}

4. Awesome-list of Weakly-supervised Learning from Our Team