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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 21–30 of 140 papers

TitleStatusHype
A Generic Visualization Approach for Convolutional Neural NetworksCode1
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object LocalizationCode1
Distilling Knowledge from Refinement in Multiple Instance Detection NetworksCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
CREAM: Weakly Supervised Object Localization via Class RE-Activation MappingCode1
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
Exploring Foveation and Saccade for Improved Weakly-Supervised LocalizationCode1
Improving Weakly-supervised Object Localization via Causal InterventionCode1
LayerCAM: Exploring Hierarchical Class Activation Maps for LocalizationCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
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