{"title":"用于多模态情感分析的单模态和多模态联合训练策略","authors":"","doi":"10.1016/j.imavis.2024.105172","DOIUrl":null,"url":null,"abstract":"<div><p>With the explosive growth of social media video content, research on multimodal sentiment analysis (MSA) has attracted considerable attention recently. Despite significant progress in MSA, there remains challenges: current research mostly focuses on learning either unimodal features or aspects of multimodal interactions, neglecting the importance of simultaneously considering both unimodal features and intermodal interactions. To address the aforementioned challenges, this paper proposes a fusion strategy called Joint Training of Unimodal and Multimodal (JTUM). Specifically, this strategy combines unimodal label generation module with cross-modal transformer. The unimodal label generation module aims to generate more distinctive labels for each unimodal input, facilitating more effective learning of unimodal representations. Meanwhile, cross-modal transformer is designed to treat each modality as a target modality and optimize it using other modalities as source modalities, thereby learning the interactions between each pair of modalities. By jointly training unimodal and multimodal tasks, our model can focus on individual modality features while learning the interactions between modalities. Finally, to better capture temporal information and make predictions, we also added self-attention transformer as sequence models. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate that JTUM outperforms current main methods.</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\":\"Joint training strategy of unimodal and multimodal for multimodal sentiment analysis\",\"authors\":\"\",\"doi\":\"10.1016/j.imavis.2024.105172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the explosive growth of social media video content, research on multimodal sentiment analysis (MSA) has attracted considerable attention recently. Despite significant progress in MSA, there remains challenges: current research mostly focuses on learning either unimodal features or aspects of multimodal interactions, neglecting the importance of simultaneously considering both unimodal features and intermodal interactions. To address the aforementioned challenges, this paper proposes a fusion strategy called Joint Training of Unimodal and Multimodal (JTUM). Specifically, this strategy combines unimodal label generation module with cross-modal transformer. The unimodal label generation module aims to generate more distinctive labels for each unimodal input, facilitating more effective learning of unimodal representations. Meanwhile, cross-modal transformer is designed to treat each modality as a target modality and optimize it using other modalities as source modalities, thereby learning the interactions between each pair of modalities. By jointly training unimodal and multimodal tasks, our model can focus on individual modality features while learning the interactions between modalities. Finally, to better capture temporal information and make predictions, we also added self-attention transformer as sequence models. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate that JTUM outperforms current main methods.</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/S0262885624002774\",\"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/S0262885624002774","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}
Joint training strategy of unimodal and multimodal for multimodal sentiment analysis
With the explosive growth of social media video content, research on multimodal sentiment analysis (MSA) has attracted considerable attention recently. Despite significant progress in MSA, there remains challenges: current research mostly focuses on learning either unimodal features or aspects of multimodal interactions, neglecting the importance of simultaneously considering both unimodal features and intermodal interactions. To address the aforementioned challenges, this paper proposes a fusion strategy called Joint Training of Unimodal and Multimodal (JTUM). Specifically, this strategy combines unimodal label generation module with cross-modal transformer. The unimodal label generation module aims to generate more distinctive labels for each unimodal input, facilitating more effective learning of unimodal representations. Meanwhile, cross-modal transformer is designed to treat each modality as a target modality and optimize it using other modalities as source modalities, thereby learning the interactions between each pair of modalities. By jointly training unimodal and multimodal tasks, our model can focus on individual modality features while learning the interactions between modalities. Finally, to better capture temporal information and make predictions, we also added self-attention transformer as sequence models. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate that JTUM outperforms current main methods.
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.