Browse > Computer Vision > Semantic Segmentation > Unsupervised Semantic Segmentation

Unsupervised Semantic Segmentation

3 papers with code · Computer Vision

Models that learn to segment each image (i.e. cluster the pixels into their ground truth classes) without seeing the ground truth labels.

( Image credit: SegSort: Segmentation by Discriminative Sorting of Segments )

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Invariant Information Clustering for Unsupervised Image Classification and Segmentation

arXiv 2019 xu-ji/IIC

The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.

SEMI-SUPERVISED IMAGE CLASSIFICATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED SEMANTIC SEGMENTATION

SegSort: Segmentation by Discriminative Sorting of Segments

ICCV 2019 jyhjinghwang/segsort

As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving $76\%$ performance of its supervised counterpart.

METRIC LEARNING UNSUPERVISED SEMANTIC SEGMENTATION

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

8 Mar 2020layumi/Seg-Uncertainty

This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.

UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED SEMANTIC SEGMENTATION