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English(EN) PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

PicoSAM3 模型可在图像传感器上实现实时分割

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 PicoSAM3，这是一种新的轻量级分割模型，专为在边缘设备上甚至直接在图像传感器上进行实时运行而设计。该模型拥有 130 万个参数，采用了密集 CNN 架构，并结合了感兴趣区域提示编码和来自大型模型的知识蒸馏等技术。PicoSAM3 在 COCO 和 LVIS 等基准测试中取得了出色的性能，其量化版本可以在 Sony IMX500 传感器上实现低于 12 毫秒的推理速度，满足其运行限制。 AI

影响 可在边缘设备和传感器上直接实现实时、注重隐私的视觉处理。

排序理由 该集群包含一篇详细介绍新模型及其性能基准的学术论文。[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 →

PicoSAM3 模型可在图像传感器上实现实时分割

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该集群包含一篇详细介绍新模型及其性能基准的学术论文。[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) · Pietro Bonazzi, Nicola Farronato, Stefan Zihlmann, Haotong Qin, Michele Magno ·

    PicoSAM3：实时传感器内感兴趣区域分割

    arXiv:2603.11917v2 Announce Type: replace Abstract: Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devices. We introduce PicoSAM3, a lightweight promptable visual segmentation model o…

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