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English(EN) Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

调查详细介绍了视频大语言模型的高成本和效率差距

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇新的调查论文审视了视频大语言模型（VideoLLMs）相关的计算成本。这些模型正变得越来越复杂，但也越来越消耗资源。该论文对VideoLLM管道不同阶段的各种推理效率机制进行了分类和分析，包括帧采样、模态编码和LLM处理。文章强调，虽然这些模型在字幕生成和问答等任务中表现出色，但其高昂的计算和内存需求限制了它们在实时或资源受限环境中的部署。该调查还指出了当前研究中的重大不足，特别是在视听效率研究和评估方法标准化方面。 AI

影响 强调需要更高效的视频大语言模型架构，以支持更广泛的实时和移动应用。

排序理由 在arXiv上发表的调查论文，详细介绍了研究结果。

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调查详细介绍了视频大语言模型的高成本和效率差距

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在arXiv上发表的调查论文，详细介绍了研究结果。
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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Killian Steunou, Yannis Tevissen, Moun\^im A. El Yacoubi ·

    视频为何依然昂贵？视频和视听大语言模型中的推理效率机制调查

    arXiv:2609.10355v1 Announce Type: cross Abstract: Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong per…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    视频为何依然昂贵？视频和视听大语言模型中的推理效率机制调查

    Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong performance on captioning, question answering, retrie…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    为什么视频仍然如此昂贵？视频和视听大语言模型中的推理效率机制调查

    This survey examines inference-efficiency techniques for video large language models, analyzing cost reductions across frame sampling, encoding, token compression, and language model stages while identifying evaluation gaps.

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