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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
Background-aware Classification Activation Map for Weakly Supervised Object LocalizationCode0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization—0
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
Category-aware Allocation Transformer for Weakly Supervised Object Localization—0
Compression and Localization in Reinforcement Learning for ATARI Games—0
Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization—0
Deep Self-Taught Learning for Weakly Supervised Object Localization—0
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