Awesome Weakly-supervised Object Localization

Table of Contents

• 1. Performance
  • 1.1. Top1/5 results on CUB-200-2011 [✓]
    • Transformer
    • VGG
    • InceptionV3
    • Others
  • 1.2. Top1/5 results on ImageNet [✓]
    • Transformer
    • VGG
    • InceptionV3
    • Others
• 2. Paper List
  • 2023 [✓]
  • 2022 [✓]
  • 2021 [✓]
  • 2020 [✓]
  • 2019 [✓]
  • 2018 [✓]
  • 2017 [✓]
  • 2016 [✓]
• 3. Dataset
  • CUB-200-2011
  • ImageNet

Table of contents generated with markdown-toc

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Contact gyguo95@gmail.com if any paper is missed!

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1. Performance

• 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.

1.1. Top1/5 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

1.2. Top1/5 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

2. Paper List

2023

• GenPromp: 2023ICCV Generative Prompt Model for Weakly Supervised Object Localization
• WEND: 2023ACM MM Rethinking the Localization in Weakly Supervised Object Localization

2022

• 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
• 2022TPAMI Evaluation for Weakly Supervised Object Localization Protocol, Metrics, and Datasets
• 2022TNNLS Diverse Complementary Part Mining for 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

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}
}