arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.07720cs.CVcs.AI
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基于视觉词的多模态自回归建模

Multi-modal Auto-regressive Modeling via Visual Words

School of Computer Science, Wuhan University(武汉大学计算机学院);Shanghai Jiao Tong University(上海交通大学);School of Information Management, Wuhan University(武汉大学信息管理学院)

Tianshuo Peng, Zuchao Li, Lefei Zhang, Hai Zhao, Ping Wang, Bo Du

AI总结:

本文提出视觉词概念,将视觉特征映射到LLM词表的概率分布以提供监督信息,首次实现统一目标下的多模态自回归建模,在多个VQA任务上验证了强大性能。

摘要:

大型语言模型受益于在海量无标注文本语料库上执行的自回归建模方法,展现出强大的感知和推理能力。然而,将自回归建模扩展到多模态场景以构建大型多模态模型时,存在一个巨大困难:图像信息在LMM中作为连续的视觉嵌入进行处理,无法获得用于分类的离散监督标签。本文首次成功以统一目标执行了多模态自回归建模。具体而言,我们提出了视觉词的概念,将视觉特征映射到LLM词表上的概率分布,为视觉建模提供监督信息。我们进一步探索了LMM内视觉特征在语义空间中的分布,以及使用文本嵌入表示视觉信息的可能性。在5个VQA任务和4个基准工具包上的实验结果和消融研究验证了所提方法的强大性能。

英文摘要:

Large Language Models (LLMs), benefiting from the auto-regressive modelling approach performed on massive unannotated texts corpora, demonstrates powerful perceptual and reasoning capabilities. However, as for extending auto-regressive modelling to multi-modal scenarios to build Large Multi-modal Models (LMMs), there lies a great difficulty that the image information is processed in the LMM as continuous visual embeddings, which cannot obtain discrete supervised labels for classification.In this paper, we successfully perform multi-modal auto-regressive modeling with a unified objective for the first time.Specifically, we propose the concept of visual tokens, which maps the visual features to probability distributions over LLM's vocabulary, providing supervision information for visual modelling.We further explore the distribution of visual features in the semantic space within LMM and the possibility of using text embeddings to represent visual information.Experimental results and ablation studies on 5 VQA tasks and 4 benchmark toolkits validate the powerful performance of our proposed approach.

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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