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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 71–80 of 140 papers

TitleStatusHype
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
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
Background-aware Classification Activation Map for Weakly Supervised Object LocalizationCode0
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization—0
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost—0
Causal Explanation of Convolutional Neural NetworksCode0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection—0
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