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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

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Showing 161–170 of 296 papers

TitleStatusHype
All-pairs Consistency Learning for Weakly Supervised Semantic SegmentationCode0
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise—0
CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics—0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors—0
Masked Collaborative Contrast for Weakly Supervised Semantic SegmentationCode0
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
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation—0
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation—0
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