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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 1–50 of 140 papers

TitleStatusHype
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
C2AM: 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
Unveiling the Potential of Structure Preserving for Weakly Supervised Object LocalizationCode1
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNsCode1
Improving Weakly-supervised Object Localization via Causal InterventionCode1
Open-World Weakly-Supervised Object LocalizationCode1
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
On Label Granularity and Object LocalizationCode1
Rethinking the Route Towards Weakly Supervised Object LocalizationCode1
Keep CALM and Improve Visual Feature AttributionCode1
Total Variation Optimization Layers for Computer VisionCode1
Weakly Supervised Object Localization as Domain AdaptionCode1
Bagging Regional Classification Activation Maps for Weakly Supervised Object LocalizationCode1
Learning Deep Features for Discriminative LocalizationCode1
CAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization PerspectiveCode1
Group-Wise Learning for Weakly Supervised Semantic SegmentationCode1
Geometry Constrained Weakly Supervised Object LocalizationCode1
Background Activation Suppression for Weakly Supervised Object Localization and Semantic SegmentationCode1
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object LocalizationCode1
Re-Attention Transformer for Weakly Supervised Object LocalizationCode1
Rethinking Class Activation Mapping for Weakly Supervised Object LocalizationCode1
LayerCAM: Exploring Hierarchical Class Activation Maps for LocalizationCode1
Spatial-Aware Token for Weakly Supervised Object LocalizationCode1
CREAM: Weakly Supervised Object Localization via Class RE-Activation MappingCode1
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localizationCode1
Localizing Objects with Self-Supervised Transformers and no LabelsCode1
ViTOL: Vision Transformer for Weakly Supervised Object LocalizationCode1
Min-max Entropy for Weakly Supervised Pointwise LocalizationCode1
Weakly Supervised Object Localization via Transformer with Implicit Spatial CalibrationCode1
Normalization Matters in Weakly Supervised Object LocalizationCode1
Generative Prompt Model for Weakly Supervised Object LocalizationCode1
Distilling Knowledge from Refinement in Multiple Instance Detection NetworksCode1
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object LocalizationCode1
Background Activation Suppression for Weakly Supervised Object LocalizationCode1
Dual-attention Guided Dropblock Module for Weakly Supervised Object LocalizationCode1
Dual Progressive Transformations for Weakly Supervised Semantic SegmentationCode1
Eigen-CAM: Class Activation Map using Principal ComponentsCode1
Shallow Feature Matters for Weakly Supervised Object LocalizationCode1
HINT: Hierarchical Neuron Concept ExplainerCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
A Generic Visualization Approach for Convolutional Neural NetworksCode1
Exploring Foveation and Saccade for Improved Weakly-Supervised LocalizationCode1
F-CAM: Full Resolution Class Activation Maps via Guided Parametric UpscalingCode1
FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object LocalizationCode1
Density-Based Region Search with Arbitrary Shape for Object Localization—0
CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization—0
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label—0
Deep Self-Taught Learning for Weakly Supervised Object Localization—0
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