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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 51–60 of 140 papers

TitleStatusHype
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained VideosCode0
Leveraging Activations for Superpixel Explanations—0
SE3D: A Framework For Saliency Method Evaluation In 3D ImagingCode0
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for HistologyCode0
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label—0
A Realistic Protocol for Evaluation of Weakly Supervised Object LocalizationCode0
Towards Two-Stream Foveation-based Active Vision Learning—0
Multiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization—0
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object LocalizationCode0
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization—0
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