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English(EN) MambaPSA: A Mamba-based Replacement for C2PSA in YOLO26

MambaPSA 取代 YOLO26 中的 C2PSA，通过 Mamba 集成提高效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 MambaPSA，这是一个旨在取代 YOLO26 对象检测框架中 C2PSA 模块的新组件。这个基于 Mamba 的模块通过减少参数和 FLOPs 来提高效率，从而在对准确性影响最小的情况下显著提高 CPU 推理吞吐量。通过集成双向 Vision Mamba (BiViM) 模块实现了进一步的增强，该模块在 PASCAL VOC 数据集上带来了显著的准确性提升。 AI

影响 这项研究展示了像 Mamba 这样的状态空间模型在不牺牲准确性的情况下提高对象检测框架效率的潜力。

排序理由 该集群包含一篇 arXiv 论文，详细介绍了现有框架的新模型组件。

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 →

MambaPSA 取代 YOLO26 中的 C2PSA，通过 Mamba 集成提高效率

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该集群包含一篇 arXiv 论文，详细介绍了现有框架的新模型组件。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Sheng-Wei Chan, Chia-Min Lin, Hsin-Jui Pan, Ching-Yu Tsai, Chih-Hsiang Yang, Yung-Che Wang, Jen-Shiun Chiang ·

    MambaPSA：YOLO26 中基于 Mamba 的 C2PSA 替代方案

    arXiv:2607.12681v1 Announce Type: new Abstract: State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppressio…

  2. arXiv cs.CV TIER_1 English(EN) · Jen-Shiun Chiang ·

    MambaPSA：YOLO26 中基于 Mamba 的 C2PSA 替代方案

    State space models (SSMs), notably Mamba, have recently emerged as efficient alternatives to self-attention with linear computational complexity. We investigate the integration of Mamba into YOLO26, the latest non-maximum suppression (NMS)-free object detection framework, by prop…

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