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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 91–100 of 140 papers

TitleStatusHype
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object LocalizationCode0
Dual-attention Guided Dropblock Module for Weakly Supervised Object LocalizationCode1
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning—0
Rethinking the Route Towards Weakly Supervised Object LocalizationCode1
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Convolutional STN for Weakly Supervised Object LocalizationCode0
Attributional Robustness Training using Input-Gradient Spatial AlignmentCode0
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information EstimatorCode0
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation—0
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