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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
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Weakly Supervised Object Localization Using Size Estimates—0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Learning Deep Features for Discriminative LocalizationCode1
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Density-Based Region Search with Arbitrary Shape for Object Localization—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
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