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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 121–130 of 140 papers

TitleStatusHype
Multiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization—0
Object-Extent Pooling for Weakly Supervised Single-Shot Localization—0
Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization—0
Rethinking the Localization in Weakly Supervised Object Localization—0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization—0
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
Towards Two-Stream Foveation-based Active Vision Learning—0
Training object class detectors with click supervision—0
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