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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
Improving Weakly-supervised Object Localization via Causal InterventionCode1
Geometry Constrained Weakly Supervised Object LocalizationCode1
HINT: Hierarchical Neuron Concept ExplainerCode1
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object LocalizationCode1
Eigen-CAM: Class Activation Map using Principal ComponentsCode1
Exploring Foveation and Saccade for Improved Weakly-Supervised LocalizationCode1
F-CAM: Full Resolution Class Activation Maps via Guided Parametric UpscalingCode1
Dual-attention Guided Dropblock Module for Weakly Supervised Object LocalizationCode1
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object LocalizationCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization PerspectiveCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
CREAM: Weakly Supervised Object Localization via Class RE-Activation MappingCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
A Generic Visualization Approach for Convolutional Neural NetworksCode1
Distilling Knowledge from Refinement in Multiple Instance Detection NetworksCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
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