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English(EN) Foresight: planning future perception in streaming VLMs without retraining

Foresight架构支持动态VLM计算而无需重新训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Foresight，一种用于流式视觉语言模型（VLMs）的新型双流架构，可在无需重新训练的情况下动态调整计算资源。该系统使用两个具有共享权重的Siamese LLM，允许一个流处理传入数据，而另一个流则预测未来内容并规划后续计算。这种预测方法使模型能够根据不断变化的场景动态调整其感知和推理，从而在OmniPro Online、StreamingBench和OVO-Bench等基准测试中取得显著的性能提升。 AI

影响 该架构可能导致AI系统中视觉流更高效、更自适应的处理。

排序理由 这是一篇详细介绍VLM新架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

Foresight架构支持动态VLM计算而无需重新训练

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这是一篇详细介绍VLM新架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ashok Prasad Neupane, Dipan Bartaula, Ankit Belbase, Saugat Adhikari, Samip Ghimire, Saroj Poudel, Binod Bhattarai, Danda Pani Paudel ·

    Foresight：在流式VLMs中规划未来感知，无需重新训练

    arXiv:2610.03123v1 Announce Type: cross Abstract: Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference. Consequently, they cannot adapt computation to evolving sce…

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