App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Multimodal named entity recognition approach based on hierarchical gating and dual contrastive supervision mechanisms

Liu Siting
Xu Yanli
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
DOI: 10.19734/j.issn.1001-3695.2025.10.0396

Abstract

The multimodal named entity recognition task still faces shortcomings in deep cross-modal interaction and entity-level alignment supervision, making it difficult for models to fully leverage fine-grained semantic correlations between images and text. To address this issue, this paper proposes a multimodal NER model, HCMCL, which integrates a hierarchical gating mechanism with dual contrastive supervision. The model first introduces a bidirectional state-space structure to enhance the ability to model long-range dependencies in text sequences and visual region features. Next, it designs a multi-level cross-modal gating structure to adaptively regulate the way visual information is injected at different semantic levels, achieving fine-grained cross-modal feature fusion. To further address the weak alignment between images and text, the authors construct a dual contrastive supervision strategy at both the sentence and entity levels to explicitly constrain modality alignment. Experimental results show that HCMCL achieves F1 scores of 76.88% and 87.96% on the Twitter-2015 and Twitter-2017 datasets, respectively, significantly outperforming current mainstream multimodal methods. Ablation studies also verify the effectiveness of each module. The study demonstrates that the proposed method effectively enhances the learning of cross-modal semantic associations and improves entity recognition performance in complex image-text scenarios.

Foundation Support

国家自然科学基金资助项目(62271303)
中国上海市教育委员会创新计划(2021-01-07-00-10-E00121)
上海自然科学基金资助项目(20ZR1423200)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.10.0396
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-20] Accepted Paper

Cite This Article

刘思婷, 徐艳丽. 基于分层门控与双重对比监督机制的多模态命名实体识别方法 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0396. (Liu Siting, Xu Yanli. Multimodal named entity recognition approach based on hierarchical gating and dual contrastive supervision mechanisms [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0396. )

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  • Application Research of Computers Monthly Journal
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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.

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