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Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.

( Image credit: Soft Proposal Networks for Weakly Supervised Object Localization )

Papers

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Showing 1–25 of 142 papers

TitleStatusHype
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level RecognitionCode2
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized CutCode2
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI PoolingCode1
LEOD: Label-Efficient Object Detection for Event CamerasCode1
Text-image Alignment for Diffusion-based PerceptionCode1
PDL: Regularizing Multiple Instance Learning with Progressive Dropout LayersCode1
Cyclic-Bootstrap Labeling for Weakly Supervised Object DetectionCode1
Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in DrawingsCode1
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
ImaginaryNet: Learning Object Detectors without Real Images and AnnotationsCode1
Object Discovery via Contrastive Learning for Weakly Supervised Object DetectionCode1
W2N:Switching From Weak Supervision to Noisy Supervision for Object DetectionCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Scaling Novel Object Detection with Weakly Supervised Detection TransformersCode1
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth BoxesCode1
SIOD: Single Instance Annotated Per Category Per Image for Object DetectionCode1
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object DetectionCode1
Weakly Supervised Rotation-Invariant Aerial Object Detection NetworkCode1
Boosting Weakly Supervised Object Detection via Learning Bounding Box AdjustersCode1
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object DetectionCode1
Domain-Adaptive Object Detection via Uncertainty-Aware Distribution AlignmentCode1
Comprehensive Attention Self-Distillation for Weakly-Supervised Object DetectionCode1
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shiftsCode1
Boosting Weakly Supervised Object Detection with Progressive Knowledge TransferCode1
Distilling Knowledge from Refinement in Multiple Instance Detection NetworksCode1
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