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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
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization PerspectiveCode1
Exploring Foveation and Saccade for Improved Weakly-Supervised LocalizationCode1
Distilling Knowledge from Refinement in Multiple Instance Detection NetworksCode1
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object LocalizationCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
Dual-attention Guided Dropblock Module for Weakly Supervised Object LocalizationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Eigen-CAM: Class Activation Map using Principal ComponentsCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
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