English(EN) LinMU: Multimodal Understanding Made Linear

LinMU 为多模态理解模型实现线性复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了 LinMU，一种新颖的视觉语言模型（VLM）架构，实现了线性复杂度，克服了当前模型二次复杂度的限制。这种新设计利用了 M-MATE 块，结合了状态空间模型和窗口注意力，以高效处理高分辨率图像和长视频。通过三阶段蒸馏过程，LinMU 在显著减少处理时间和提高吞吐量的同时，达到了现有模型的性能，使先进的多模态推理更加易于访问。 AI

影响能够更有效地处理高分辨率图像和长视频，可能导致先进多模态推理的更广泛应用。

排序理由这是一篇详细介绍新模型架构和训练方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hongjie Wang, Niraj K. Jha · 2026-05-05 04:00

LinMU：多模态理解变得线性化

arXiv:2601.01322v2 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents their deployment on edge devices and makes their understanding of high-resolution …

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报道来源 [1]

LinMU：多模态理解变得线性化

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