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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 181–190 of 296 papers

TitleStatusHype
MuSCLe: A Multi-Strategy Contrastive Learning Framework for Weakly Supervised Semantic Segmentation—0
Neural Diffusion Distance for Image Segmentation—0
NoPeopleAllowed: The Three-Step Approach to Weakly Supervised Semantic Segmentation—0
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach—0
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model—0
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation—0
P2Seg: Pointly-supervised Segmentation via Mutual Distillation—0
PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision—0
P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors—0
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds—0
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