Weakly-Supervised Object Localization
76 papers with code • 8 benchmarks • 3 datasets
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.
Benchmarks
These leaderboards are used to track progress in Weakly-Supervised Object Localization
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Libraries
Use these libraries to find Weakly-Supervised Object Localization models and implementations
ImageNet
CUB-200-2011
Tiny ImageNet
Most implemented papers
Localizing Objects with Self-Supervised Transformers and no Labels
valeoai/LOST
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We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Background Activation Suppression for Weakly Supervised Object Localization
wpy1999/bas
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CVPR 2022
Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator.
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
cvi-szu/ccam
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While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
164140757/scm
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Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications.
Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets.
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
vadimkantorov/contextlocnet
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The additive model encourages the predicted object region to be supported by its surrounding context region.
Soft Proposal Networks for Weakly Supervised Object Localization
yeezhu/SPN.pytorch
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ICCV 2017
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
Progressive Representation Adaptation for Weakly Supervised Object Localization
In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.
Self-produced Guidance for Weakly-supervised Object Localization
A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks.
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
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Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
