AI RESEARCH
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
arXiv CS.CV
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ArXi:2601.02018v2 Announce Type: replace Segment Anything Models (SAMs), known for their exceptional zero-shot segmentation performance, have garnered significant attention in the research community. Nevertheless, their performance drops significantly on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM++, which utilizes Generative Latent space Enhancement to boost robustness on low-quality images, thus enabling generalization across various image qualities.