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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
Weakly Supervised Object Localization as Domain AdaptionCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object LocalizationCode1
Localizing Objects with Self-Supervised Transformers and no LabelsCode1
F-CAM: Full Resolution Class Activation Maps via Guided Parametric UpscalingCode1
Shallow Feature Matters for Weakly Supervised Object LocalizationCode1
Normalization Matters in Weakly Supervised Object LocalizationCode1
LayerCAM: Exploring Hierarchical Class Activation Maps for LocalizationCode1
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