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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 126–142 of 142 papers

TitleStatusHype
Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection—0
Read, look and detect: Bounding box annotation from image-caption pairs—0
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection—0
Salvage of Supervision in Weakly Supervised Object Detection—0
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection—0
Self-supervised object detection from audio-visual correspondence—0
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection—0
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection—0
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction—0
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection—0
Toward Joint Thing-and-Stuff Mining for Weakly Supervised Panoptic Segmentation—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection—0
Towards Object Detection from Motion—0
Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information—0
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection—0
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images—0
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