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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 11–20 of 140 papers

TitleStatusHype
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
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
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Total Variation Optimization Layers for Computer VisionCode1
HINT: Hierarchical Neuron Concept ExplainerCode1
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