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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 31–40 of 140 papers

TitleStatusHype
Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization—0
Re-Attention Transformer for Weakly Supervised Object LocalizationCode1
Weakly Supervised Object Localization via Transformer with Implicit Spatial CalibrationCode1
On Label Granularity and Object LocalizationCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
CREAM: Weakly Supervised Object Localization via Class RE-Activation MappingCode1
Location-free Human Pose Estimation—0
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition—0
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
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