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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 131–140 of 140 papers

TitleStatusHype
Two-Phase Learning for Weakly Supervised Object Localization—0
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection—0
Weakly Supervised Localization Using Background Images—0
Weakly Supervised Object Localization and Detection: A Survey—0
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset—0
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
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