Segment scene unsupervised using close features have similar semantics with SmooSeg
Segment scene unsupervised using close features have similar semantics with SmooSeg
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2310.17874
arXiv PDF paper https://arxiv.org/pdf/2310.17874.pdf
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
Prior works ... leveraging prior knowledge of semantic consistency or priori concepts from self-supervised learning methods, which ... overlook the coherence ... of image segments.
... demonstrate that the smoothness prior, asserting that close features in a metric space share the same semantics, can significantly simplify segmentation by casting unsupervised semantic segmentation as an energy minimization problem.
... propose a novel approach called SmooSeg that harnesses self-supervised learning methods to model the closeness relationships among observations as smoothness signals.
... introduce a novel smoothness loss that promotes piecewise smoothness within segments while preserving discontinuities across different segments.
... SmooSeg significantly outperforms STEGO in terms of pixel accuracy ...
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