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arXiv 2503.19474cs.CVcs.AI

A-MESS:用于多模态意图识别的基于锚点的多模态嵌入与语义同步

A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition

Nanchang Research Institute of Zhejiang University(浙江大学南昌研究院);Jiangxi Qiushi Higher Research Institute(江西求是高等研究院)

Yaomin Shen, Xiaojian Lin, Wei Fan

AI总结:

针对多模态意图识别中模态内在联系与语义表示不足的问题,提出A-MESS框架,通过基于锚点的多模态嵌入模块融合输入,并利用三元组对比学习语义同步策略优化表示,实验证明其达到SOTA水平。

摘要:

在多模态意图识别(MIR)领域,目标是通过整合语言文本、身体手势和语调等多种模态来识别人类意图。然而,现有方法难以充分捕捉模态间的内在联系,并忽略了意图对应的语义表示。为解决这些局限性,我们提出了基于锚点的多模态嵌入与语义同步(A-MESS)框架。我们首先设计了基于锚点的多模态嵌入(A-ME)模块,采用基于锚点的嵌入融合机制整合多模态输入。此外,我们开发了语义同步(SS)策略及三元组对比学习流程,通过将多模态表示与大型语言模型生成的标签描述同步来优化该过程。综合实验表明,我们的A-MESS达到了最先进水平,并为多模态表示和下游任务提供了深刻见解。

英文摘要:

In the domain of multimodal intent recognition (MIR), the objective is to recognize human intent by integrating a variety of modalities, such as language text, body gestures, and tones. However, existing approaches face difficulties adequately capturing the intrinsic connections between the modalities and overlooking the corresponding semantic representations of intent. To address these limitations, we present the Anchor-based Multimodal Embedding with Semantic Synchronization (A-MESS) framework. We first design an Anchor-based Multimodal Embedding (A-ME) module that employs an anchor-based embedding fusion mechanism to integrate multimodal inputs. Furthermore, we develop a Semantic Synchronization (SS) strategy with the Triplet Contrastive Learning pipeline, which optimizes the process by synchronizing multimodal representation with label descriptions produced by the large language model. Comprehensive experiments indicate that our A-MESS achieves state-of-the-art and provides substantial insight into multimodal representation and downstream tasks.

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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