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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
Dual-attention Guided Dropblock Module for Weakly Supervised Object LocalizationCode1
Rethinking the Route Towards Weakly Supervised Object LocalizationCode1
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Min-max Entropy for Weakly Supervised Pointwise LocalizationCode1
Learning Deep Features for Discriminative LocalizationCode1
Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization—0
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI LocalizationCode0
TeD-Loc: Text Distillation for Weakly Supervised Object LocalizationCode0
Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning—0
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