SOTAVerified

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 171180 of 296 papers

TitleStatusHype
Coupling Global Context and Local Contents for Weakly-Supervised Semantic SegmentationCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
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
Exploring Weakly Supervised Semantic Segmentation Ensembles for Medical Imaging SystemsCode0
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical ImagesCode0
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation0
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization0
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation0
Show:102550
← PrevPage 18 of 30Next →

No leaderboard results yet.