SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 140 papers

TitleStatusHype
Fine-Grained Attention for Weakly Supervised Object Localization—0
DAP: Detection-Aware Pre-training with Weak SupervisionCode0
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object LocalizationCode1
Unveiling the Potential of Structure Preserving for Weakly Supervised Object LocalizationCode1
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection—0
GridMix: Strong regularization through local context mappingCode0
Foreground Activation Maps for Weakly Supervised Object Localization—0
Hierarchical Complementary Learning for Weakly Supervised Object Localization—0
LID 2020: The Learning from Imperfect Data Challenge Results—0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
Show:102550
← PrevPage 8 of 14Next →

No leaderboard results yet.