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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 101–110 of 140 papers

TitleStatusHype
Weakly Supervised Object Localization Using Size Estimates—0
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation—0
SE3D: A Framework For Saliency Method Evaluation In 3D ImagingCode0
Self-produced Guidance for Weakly-supervised Object LocalizationCode0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
Causal Explanation of Convolutional Neural NetworksCode0
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic VideosCode0
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