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Weakly-Supervised Semantic Segmentation

The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (expensive pixel-level annotations). However, in the weakly-supervised setting, the dataset consists of images and corresponding annotations that are relatively easy to obtain, such as tags/labels of objects present in the image.

( Image credit: Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing )

Papers

Showing 101110 of 296 papers

TitleStatusHype
Removing supervision in semantic segmentation with local-global matching and area balancingCode0
MECPformer: Multi-estimations Complementary Patch with CNN-Transformers for Weakly Supervised Semantic SegmentationCode0
Extracting Class Activation Maps from Non-Discriminative Features as wellCode1
Exploring Weakly Supervised Semantic Segmentation Ensembles for Medical Imaging SystemsCode0
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation0
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical ImagesCode0
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation0
Token Contrast for Weakly-Supervised Semantic SegmentationCode1
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic SegmentationCode1
FPR: False Positive Rectification for Weakly Supervised Semantic SegmentationCode1
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