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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
Hierarchical Complementary Learning for Weakly Supervised Object Localization—0
How hard can it be? Estimating the difficulty of visual search in an image—0
Improved Techniques For Weakly-Supervised Object Localization—0
Improving Few-shot Learning with Weakly-supervised Object Localization—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label—0
Information Entropy Based Feature Pooling for Convolutional Neural Networks—0
Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning—0
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization—0
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
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