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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
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationCode0
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection—0
Deep Self-Taught Learning for Weakly Supervised Object Localization—0
Multiple Instance Detection Network with Online Instance Classifier RefinementCode1
Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning—0
Weakly Supervised Cascaded Convolutional Networks—0
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization—0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Weakly supervised object detection using pseudo-strong labels—0
Self Paced Deep Learning for Weakly Supervised Object DetectionCode0
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection—0
Weakly Supervised Localization using Deep Feature Maps—0
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection—0
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks—0
Weakly Supervised Deep Detection NetworksCode0
Weakly Supervised Object Detection With Convex Clustering—0
On learning to localize objects with minimal supervision—0
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