Weakly-Supervised Object Localization
77 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
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization
Sierkinhane/ORNet
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ICCV 2021
In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner.
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.
F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Interpolation is required to restore full size CAMs, yet it does not consider the statistical properties of objects, such as color and texture, leading to activations with inconsistent boundaries, and inaccurate localizations.
Causal Explanation of Convolutional Neural Networks
Since CNNs form a hierarchical structure, and since causal models can be hierarchically abstracted, we employ this similarity to perform the most important contribution of this paper, which is localizing the important features in the input image that contributed the most to a CNN’s decision.
Shallow Feature Matters for Weakly Supervised Object Localization
In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly.
Normalization Matters in Weakly Supervised Object Localization
GenDisc/IVR
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ICCV 2021
Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information.
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
jacobgil/pytorch-grad-cam
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IEEE 2021
To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.
Strengthen Learning Tolerance for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision.
Keep CALM and Improve Visual Feature Attribution
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks.
Improving Weakly-supervised Object Localization via Causal Intervention
The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels.
