Browse > Computer Vision > Semantic Segmentation > Unsupervised Semantic Segmentation

# Unsupervised Semantic Segmentation Edit

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

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.

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# SegSort: Segmentation by Discriminative Sorting of Segments

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.

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# 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.

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