研究人员开发了 GleSAM++，这是 Segment Anything Models (SAMs) 的一种增强方法，旨在提高低质量或降质图像的图像分割性能。该方法使用生成式潜在空间增强和一种新颖的降质感知自适应增强机制，根据图像降质程度来预测和重建特征。这种方法使 SAMs 在保持对清晰图像泛化能力的同时，显著提高了对训练中未见的复杂降质的鲁棒性。 AI
影响 增强了基础分割模型在图像质量下降的实际应用中的鲁棒性。
排序理由 这是一篇详细介绍改进现有模型新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2601.02018v2 Announce Type: replace Abstract: 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 degra…
arXiv:2503.12507v3 Announce Type: replace Abstract: Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, w…