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English(EN) Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation

新方法利用多模态伪标签和测试时自适应来增强开放词汇分割

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了用于开放词汇实例和全景分割的新方法，旨在识别预定义类别之外的对象，而无需大量手动标注。一种方法，在 arXiv 论文中有所详述，使用 Grounded SAM 和 LLaVA 等模型生成的多模态伪标签，并通过 CLIP 引导过滤和 GPT 驱动的字幕重建进行增强。另一种方法，测试时原型自适应 (TPA)，是一种无训练的即插即用模块，在输出层面运行，使用未标注的部署域图像从 DINO 特征构建类别原型，以提高分割准确性。 AI

影响 这些进展可能带来更通用、更准确的图像识别系统，能够理解更广泛的对象而无需大量手动标注。

排序理由 两篇 arXiv 论文详细介绍了开放词汇分割的新颖方法。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新方法利用多模态伪标签和测试时自适应来增强开放词汇分割

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两篇 arXiv 论文详细介绍了开放词汇分割的新颖方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang ·

    从多模态伪标签中学习，实现鲁棒的开放词汇实例和全景分割

    arXiv:2608.11681v1 Announce Type: cross Abstract: This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations.…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    从多模态伪标签中学习，实现鲁棒的开放词汇实例和全景分割

    This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations. Existing methods often suffer from noisy pseudo-m…

  3. arXiv cs.CV TIER_1 English(EN) · Haozhe Wang, Jintao Cheng, Weibin Li, Xiaoyu Tang ·

    面向开放词汇语义分割的测试时原型自适应

    arXiv:2608.08290v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) repurposes a pretrained CLIP encoder for dense prediction without additional labeled supervision. Existing methods improve CLIP's spatial behavior either by redesigning its internal atten…

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