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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

Multimodal emotion recognition in conversation based on hypergraph learning and pairwise cross modal fusion

Li Shangwang1a
Miao Yuqing1a,1b
Liu Tonglai2
Zhang Wanzhen2
Zhou Ming3
1. a. School of Computer Science & Information Security, b. Guangxi Key Laboratory of Image & Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
2. College of artificial intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
3. Guilin Hivision Technology Company, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0233

Abstract

To address issues such as insufficient utilization of interaction information between modalities and multivariate dialogue relations in current multimodal emotion recognition in conversation models, this paper proposed a multimodal emotion recognition in conversation model based on hypergraph learning and pairwise cross-modal fusion. In the hypergraph learning module of the model, discourse representations are taken as nodes, and two different types of hyperedges containing multimodal and temporal information are designed to form a hypergraph. Hypergraph convolution is used to capture multivariate dialogue relations between speakers. Meanwhile, a dual-stream gated attention network is proposed to dynamically adjust node features and reduce information redundancy. In the pairwise cross-modal fusion module, each modality is used as a baseline feature, and based on the cross-modal attention mechanism, it is repeatedly reinforced with other modal features to excavate deep interaction information between pairwise modalities and enhance cross-modal feature representation. Experimental results show that on the IEMOCAP and CMU-MOSEI datasets, the accuracy and weighted average F1 score of the proposed model are better than those of multiple comparison models, fully verifying the effectiveness of the model.

Foundation Support

国家自然科学基金资助项目(62366010,62366011)
广东省自然科学基金资助项目(2023A1515011230)
广东省哲学社会科学规划专项项目(GD25CW04)
桂林电子科技大学研究生教育创新计划资助项目(2025YCXS076)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0233
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-25] Accepted Paper

Cite This Article

李尚往, 缪裕青, 刘同来, 等. 基于超图学习与成对跨模态融合的多模态对话情绪识别 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0233. (Li Shangwang, Miao Yuqing, Liu Tonglai, et al. Multimodal emotion recognition in conversation based on hypergraph learning and pairwise cross modal fusion [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0233. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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