{"title":"多任务减少分歧多模态情感融合网络","authors":"","doi":"10.1016/j.imavis.2024.105158","DOIUrl":null,"url":null,"abstract":"<div><p>Existing multimodal sentiment analysis models can effectively capture sentimental commonalities between different modalities and possess high sentimental acquisition capability. However, there are still shortcomings in the model's analysis and recognition abilities when dealing with samples that exhibit sentimental polarity disagreement between different modalities. Additionally, the dominance of the text modality in multimodal models, particularly those pre-trained with BERT, can hinder the learning of other modalities due to its richer semantic information. This issue becomes particularly pronounced in cases where there is a conflict between multimodal and textual sentimental polarities, often leading to suboptimal analytical results. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, We propose a Multi-Task disagreement-Reducing Multimodal Sentiment Fusion Network (MtDr-MSF), designed to enhance the semantic information of non-text modalities and reduce the dominant impact of the textual modality on the model, and to improve the learning capabilities of unimodal networks. We conducted experiments on multimodal sentiment analysis datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The results show that our method outperforms the current SOTA method.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-task disagreement-reducing multimodal sentiment fusion network\",\"authors\":\"\",\"doi\":\"10.1016/j.imavis.2024.105158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Existing multimodal sentiment analysis models can effectively capture sentimental commonalities between different modalities and possess high sentimental acquisition capability. However, there are still shortcomings in the model's analysis and recognition abilities when dealing with samples that exhibit sentimental polarity disagreement between different modalities. Additionally, the dominance of the text modality in multimodal models, particularly those pre-trained with BERT, can hinder the learning of other modalities due to its richer semantic information. This issue becomes particularly pronounced in cases where there is a conflict between multimodal and textual sentimental polarities, often leading to suboptimal analytical results. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, We propose a Multi-Task disagreement-Reducing Multimodal Sentiment Fusion Network (MtDr-MSF), designed to enhance the semantic information of non-text modalities and reduce the dominant impact of the textual modality on the model, and to improve the learning capabilities of unimodal networks. We conducted experiments on multimodal sentiment analysis datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The results show that our method outperforms the current SOTA method.</p></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885624002634\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624002634","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
现有的多模态情感分析模型能有效捕捉不同模态之间的情感共性,并具有较高的情感获取能力。然而,在处理不同模态之间情感极性不一致的样本时,模型的分析和识别能力仍存在不足。此外,在多模态模型中,尤其是使用 BERT 预先训练的模型中,文本模态占主导地位,由于其语义信息更为丰富,可能会阻碍其他模态的学习。这一问题在多模态和文本情感两极冲突的情况下尤为突出,往往会导致分析结果不理想。此外,单任务学习也会抑制每种模态的分类能力。在本文中,我们提出了多任务分歧降低多模态情感融合网络(MtDr-MSF),旨在增强非文本模态的语义信息,降低文本模态对模型的主导影响,并提高单模态网络的学习能力。我们在多模态情感分析数据集 CMU-MOSI、CMU-MOSEI 和 CH-SIMS 上进行了实验。结果表明,我们的方法优于当前的 SOTA 方法。
Existing multimodal sentiment analysis models can effectively capture sentimental commonalities between different modalities and possess high sentimental acquisition capability. However, there are still shortcomings in the model's analysis and recognition abilities when dealing with samples that exhibit sentimental polarity disagreement between different modalities. Additionally, the dominance of the text modality in multimodal models, particularly those pre-trained with BERT, can hinder the learning of other modalities due to its richer semantic information. This issue becomes particularly pronounced in cases where there is a conflict between multimodal and textual sentimental polarities, often leading to suboptimal analytical results. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, We propose a Multi-Task disagreement-Reducing Multimodal Sentiment Fusion Network (MtDr-MSF), designed to enhance the semantic information of non-text modalities and reduce the dominant impact of the textual modality on the model, and to improve the learning capabilities of unimodal networks. We conducted experiments on multimodal sentiment analysis datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The results show that our method outperforms the current SOTA method.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.