基于视觉语言模型的跨模态多级融合情感分析方法

doi:10.16451/j.cnki.issn1003-6059.202405007

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Abstract

Image-text multimodal sentiment analysis aims to predict sentimental polarity by integrating visual modalities and text modalities. The key to solving the multimodal sentiment analysis task is obtaining high-quality multimodal representations of both visual and textual modalities and achieving efficient fusion of these representations. Therefore, a cross-modal multi-level fusion sentiment analysis method based on visual language model(MFVL) is proposed. Firstly, based on the pre-trained visual language model, high-quality multimodal representations and modality bridge representations are generated by freezing the parameters and a low-rank adaptation method being adopted for fine-tuning the large language model. Secondly, a cross-modal multi-head co-attention fusion module is designed to perform interactive weighted fusion of the visual and textual modality representations respectively. Finally, a mixture of experts module is designed to deeply fuse the visual, textual and modality bridging representations to achieve multimodal sentiment analysis. Experimental results indicate that MFVL achieves state-of-the-art performance on the public evaluation datasets MVSA-Single and HFM.

Key words： Visual Language Model Multimodal Fusion Multi-head Attention Mixture of Experts Network Sentiment Analysis

Received: 15 February 2024

ZTFLH:

TP391.1

Fund:

National Key Research and Development Program of China(No.2023YFB3308004), National Natural Science Founda-tion of China(No.92267107)

Corresponding Authors: DU Yongping, Ph.D., professor. Her research interests include information retrieval, information extraction and natural language processing.

About author:: XIE Runfeng, Master student. His research interests include natural language processing and multimodal sentiment analysis. ZHANG Bochao, Master student. His research interests include natural language processing.

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	XIE Runfeng
	ZHANG Bochao
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Cite this article:

XIE Runfeng,ZHANG Bochao,DU Yongping. Cross-Modal Multi-level Fusion Sentiment Analysis Method Based on Visual Language Model[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(5): 459-468.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202405007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2024/V37/I5/459

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