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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 81–90 of 140 papers

TitleStatusHype
Strengthen Learning Tolerance for Weakly Supervised Object LocalizationCode0
Improving Few-shot Learning with Weakly-supervised Object Localization—0
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization—0
Weakly Supervised Object Localization and Detection: A Survey—0
Fine-Grained Attention for Weakly Supervised Object Localization—0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
GridMix: Strong regularization through local context mappingCode0
Foreground Activation Maps for Weakly Supervised Object Localization—0
Hierarchical Complementary Learning for Weakly Supervised Object Localization—0
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