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English(EN) MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation

新方法增强域自适应全景分割

研究人员开发了两种新的域自适应全景分割方法,这是一种用于识别和描绘图像中对象的_技术。MC-PanDA++通过利用自监督视觉编码器和单阶段训练流程,提供了一种更简单、更鲁棒的方法,改进了其前身MC-PanDA。另一方面,ProGuT专注于森林场景的标签高效分割,在没有每张图像训练掩码的情况下生成伪标签,并在全景质量和实例分离方面取得了显著改进。 AI

影响 域自适应和标签高效分割的这些进步可以提高各种应用中_驱动的图像分析的准确性并降低成本。

排序理由 该集群包含两篇详细介绍计算机视觉研究新方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新方法增强域自适应全景分割

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ivan Martinovi\'c, Josip \v{S}ari\'c, Yuki M. Asano, Sini\v{s}a \v{S}egvi\'c ·

    MC-PanDA++:更简单、更强大、更鲁棒的域自适应全景分割

    arXiv:2609.39681v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g., synthetic) and an unlabeled target domain to bridge the distribution gap. Exis…

  2. arXiv cs.CV TIER_1 English(EN) · Pankaj Deoli, Karsten Berns ·

    ProGuT:林区标签高效全景分割

    arXiv:2609.36891v1 Announce Type: new Abstract: Panoptic segmentation in forest environments is bottlenecked not by semantic quality but by instance separation; existing unsupervised panoptic approaches produce usable stuff maps but near-zero thing quality. Depth or flow-based in…