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 91–100 of 140 papers

TitleStatusHype
LID 2020: The Learning from Imperfect Data Challenge Results—0
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels—0
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object LocalizationCode0
Entropy Guided Adversarial Model for Weakly Supervised Object Localization—0
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement MapsCode0
Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization—0
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object LocalizationCode0
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning—0
Convolutional STN for Weakly Supervised Object LocalizationCode0
Attributional Robustness Training using Input-Gradient Spatial AlignmentCode0
Show:102550
← PrevPage 10 of 14Next →

No leaderboard results yet.