arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.14520cs.CV
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Cobra:将 Mamba 扩展到多模态大语言模型以实现高效推理

Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference

Zhejiang University(浙江大学);Westlake University(西湖大学);DAMO Academy, Alibaba Group(阿里巴巴达摩院)

Han Zhao, Min Zhang, Wei Zhao, Pengxiang Ding, Siteng Huang, Donglin Wang

AI总结:

本文提出 Cobra,将 Mamba 扩展为线性复杂度的多模态大语言模型,并通过多种模态融合方案实现高效推理,在性能可比的同时显著减少参数并提升速度。

摘要:

近年来,多模态大语言模型(MLLM)在各个领域的应用取得了显著成功。然而,作为许多下游任务的基础模型,当前的 MLLM 由众所周知的 Transformer 网络构成,其计算复杂度为二次方,效率较低。为提高此类基础模型的效率,我们提出了 Cobra,一种具有线性计算复杂度的 MLLM。具体而言,Cobra 将高效的 Mamba 语言模型集成到视觉模态中。此外,我们探索并研究了多种模态融合方案,以构建有效的多模态 Mamba。大量实验表明:(1)Cobra 取得了与当前计算高效的最先进方法(例如 LLaVA-Phi、TinyLLaVA 和 MobileVLM v2)极具竞争力的性能,并且由于 Cobra 的线性序列建模,其速度更快。(2)有趣的是,在具有挑战性的闭集预测基准上的结果表明,Cobra 在克服视觉错觉和空间关系判断方面表现良好。(3)值得注意的是,Cobra 仅使用约 43% 的参数量,甚至取得了与 LLaVA 相当的性能。我们将开源 Cobra 的全部代码,并希望所提出的方法能够推动未来关于 MLLM 中复杂度问题的研究。我们的项目页面位于:https://sites.google.com/view/cobravlm。

英文摘要:

In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream tasks, current MLLMs are composed of the well-known Transformer network, which has a less efficient quadratic computation complexity. To improve the efficiency of such basic models, we propose Cobra, a linear computational complexity MLLM. Specifically, Cobra integrates the efficient Mamba language model into the visual modality. Moreover, we explore and study various modal fusion schemes to create an effective multi-modal Mamba. Extensive experiments demonstrate that (1) Cobra achieves extremely competitive performance with current computationally efficient state-of-the-art methods, e.g., LLaVA-Phi, TinyLLaVA, and MobileVLM v2, and has faster speed due to Cobra's linear sequential modeling. (2) Interestingly, the results of closed-set challenging prediction benchmarks show that Cobra performs well in overcoming visual illusions and spatial relationship judgments. (3) Notably, Cobra even achieves comparable performance to LLaVA with about 43% of the number of parameters. We will make all codes of Cobra open-source and hope that the proposed method can facilitate future research on complexity problems in MLLM. Our project page is available at: https://sites.google.com/view/cobravlm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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