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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 111–120 of 140 papers

TitleStatusHype
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
Learning Instance Activation Maps for Weakly Supervised Instance Segmentation—0
Leveraging Activations for Superpixel Explanations—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Location-free Human Pose Estimation—0
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization—0
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization—0
ML-LocNet: Improving Object Localization with Multi-view Learning Network—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization—0
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