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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 41–50 of 140 papers

TitleStatusHype
Total Variation Optimization Layers for Computer VisionCode1
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization—0
HINT: Hierarchical Neuron Concept ExplainerCode1
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
Weakly Supervised Object Localization as Domain AdaptionCode1
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized CutCode2
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Background-aware Classification Activation Map for Weakly Supervised Object LocalizationCode0
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