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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 126–140 of 140 papers

TitleStatusHype
Object-Extent Pooling for Weakly Supervised Single-Shot Localization—0
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and SegmentationCode0
How hard can it be? Estimating the difficulty of visual search in an image—0
Training object class detectors with click supervision—0
Deep Self-Taught Learning for Weakly Supervised Object Localization—0
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action LocalizationCode0
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationCode0
Weakly Supervised Object Localization Using Size Estimates—0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Improving Weakly-Supervised Object Localization By Micro-Annotation—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning—0
Density-Based Region Search with Arbitrary Shape for Object Localization—0
Multi-fold MIL Training for Weakly Supervised Object Localization—0
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