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English(EN) Hear to See: Discerning Stateful Listening for Audio-Visual Instance Segmentation

新的Hear to See方法推进了音频-视觉实例分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Hear to See (H2S)的新方法，以改进音频-视觉实例分割。该技术解决了将重叠的声音事件与视觉实例匹配以及处理音频和视觉信号之间时间错位等挑战。H2S利用声学语义投影仪来解缠混合音频并建立分层对应关系，并利用音频调制的Mamba异步动态调制器进行自适应状态转换，以实现鲁棒跟踪。 AI

影响 这项研究通过更有效地整合音频和视觉信息，提升了模型理解和分割现实世界的能力。

排序理由 该集群描述了arXiv论文中提出的一种用于特定计算机视觉任务的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的Hear to See方法推进了音频-视觉实例分割

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该集群描述了arXiv论文中提出的一种用于特定计算机视觉任务的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Leiye Liu, Miao Zhang, Jiahong Jiang, Jingjing Li, Jialong Zhong, Kai Peng, Tingwei Liu, Wei Ji, Yongri Piao, Huchuan Lu ·

    Hear to See：区分用于视听实例分割的状态监听

    arXiv:2608.03264v1 Announce Type: cross Abstract: Audio-visual instance segmentation (AVIS) requires accurately identifying and tracking individual sounding objects with pixel-level masks. Existing methods struggle to match overlapping acoustic events with visual instances and ha…

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