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
Latest papers
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation
On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM.
Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision.
Re-Attention Transformer for Weakly Supervised Object Localization
su-hui-zz/reattentiontransformer
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Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.
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.
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization
Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL).
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
jazzcharles/cream
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CVPR 2022
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
ViTOL: Vision Transformer for Weakly Supervised Object Localization
saurav-31/vitol
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Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise.
Total Variation Optimization Layers for Computer Vision
raymondyeh07/tv_layers_for_cv
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CVPR 2022
To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision.
