OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation

arXiv:2403.06546v1 Announce Type: cross
Abstract: Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing methods utilize features generated by self-supervised models and specific priors for clustering. However, their clustering objectives are not involved in the optimization of the features during training. Additionally, due to the lack of clear class definitions in USS, the resulting segments may not align well with the clustering objective. In this paper, we …

abstract arxiv clustering cs.cv cs.lg features generated however human images labeling labels optimization segmentation semantic sparsity type unsupervised via