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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
Attention-based Dropout Layer for Weakly Supervised Object LocalizationCode0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Convolutional STN for Weakly Supervised Object LocalizationCode0
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object LocalizationCode0
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object LocalizationCode0
DANet: Divergent Activation for Weakly Supervised Object LocalizationCode0
Min-Entropy Latent Model for Weakly Supervised Object DetectionCode0
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A SurveyCode0
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object LocalizationCode0
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained VideosCode0
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