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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 91100 of 296 papers

TitleStatusHype
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic SegmentationCode0
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic SegmentationCode0
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic SegmentationCode1
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation0
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation0
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation ProblemsCode1
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic SegmentationCode1
Coupling Global Context and Local Contents for Weakly-Supervised Semantic SegmentationCode0
High-fidelity Pseudo-labels for Boosting Weakly-Supervised SegmentationCode0
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic SegmentationCode1
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