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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 1–25 of 140 papers

TitleStatusHype
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized CutCode2
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization PerspectiveCode1
Exploring Foveation and Saccade for Improved Weakly-Supervised LocalizationCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object LocalizationCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
Open-World Weakly-Supervised Object LocalizationCode1
Spatial-Aware Token for Weakly Supervised Object LocalizationCode1
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
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
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Total Variation Optimization Layers for Computer VisionCode1
HINT: Hierarchical Neuron Concept ExplainerCode1
Weakly Supervised Object Localization as Domain AdaptionCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object LocalizationCode1
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