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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 26–50 of 140 papers

TitleStatusHype
Adversarial Normalization: I Can Visualize Everything (ICE)Code0
Expeditious Saliency-guided Mix-up through Random Gradient ThresholdingCode0
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization—0
Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization—0
Re-Attention Transformer for Weakly Supervised Object LocalizationCode1
Weakly Supervised Object Localization via Transformer with Implicit Spatial CalibrationCode1
On Label Granularity and Object LocalizationCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
CREAM: Weakly Supervised Object Localization via Class RE-Activation MappingCode1
Location-free Human Pose Estimation—0
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition—0
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Total Variation Optimization Layers for Computer VisionCode1
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization—0
HINT: Hierarchical Neuron Concept ExplainerCode1
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization—0
Weakly Supervised Object Localization as Domain AdaptionCode1
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized CutCode2
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Background-aware Classification Activation Map for Weakly Supervised Object LocalizationCode0
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