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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
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Self Paced Deep Learning for Weakly Supervised Object DetectionCode0
Weakly Supervised Object Detection for Automatic Tooth-marked Tongue RecognitionCode0
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification FrameworkCode0
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object DetectionCode0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Gall Bladder Cancer Detection from US Images with Only Image Level LabelsCode0
CaT: Weakly Supervised Object Detection with Category TransferCode0
Smart Feature is What You NeedCode0
Soft Proposal Networks for Weakly Supervised Object LocalizationCode0
Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data VolumeCode0
A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial AnnotationsCode0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical videoCode0
VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object DetectionCode0
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationCode0
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