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Weakly-Supervised Object Localization

Weakly supervised object localization (WSOL) learns to localize objects with only image-level labels, no object level labels (bonding boxes, etc.,) is needed. It is more attractive since image-level labels are much easier and cheaper to obtain.

Papers

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Showing 1–10 of 140 papers

TitleStatusHype
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized CutCode2
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
A Generic Visualization Approach for Convolutional Neural NetworksCode1
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization PerspectiveCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object LocalizationCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
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