{"total":21,"rows":[{"id":"2109.01797","title":"Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis","created":"2021-09-04","cited_by_count":41,"area":null},{"id":"2104.07123","title":"The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress","created":"2021-04-14","cited_by_count":27,"area":null},{"id":"2207.05691","title":"The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress","created":"2022-06-23","cited_by_count":23,"area":null},{"id":"2208.07589","title":"Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis","created":"2022-08-16","cited_by_count":20,"area":null},{"id":"2107.01175","title":"Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion","created":"2021-07-02","cited_by_count":15,"area":null},{"id":"2409.07388","title":"Recent Advances in Multimodal Affective Computing: An NLP Perspective","created":"2024-09-11","cited_by_count":13,"area":null},{"id":"2305.07611","title":"Multimodal Sentiment Analysis: A Survey","created":"2023-05-12","cited_by_count":12,"area":null},{"id":"2201.03804","title":"CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition","created":"2022-01-11","cited_by_count":10,"area":null},{"id":"2107.11757","title":"MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox","created":"2021-07-25","cited_by_count":8,"area":null},{"id":"2209.14085","title":"PTSD in the Wild: A Video Database for Studying Post-Traumatic Stress Disorder Recognition in Unconstrained Environments","created":"2022-09-28","cited_by_count":6,"area":null},{"id":"2310.17212","title":"Affective Video Content Analysis: Decade Review and New Perspectives","created":"2023-10-26","cited_by_count":5,"area":null},{"id":"2110.08495","title":"Hybrid Mutimodal Fusion for Dimensional Emotion Recognition","created":"2021-10-16","cited_by_count":4,"area":null},{"id":"2403.08824","title":"Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey","created":"2024-03-09","cited_by_count":4,"area":null},{"id":"2311.11250","title":"A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications","created":"2023-11-19","cited_by_count":3,"area":null},{"id":"2505.17501","title":"RoHyDR: Robust Hybrid Diffusion Recovery for Incomplete Multimodal Emotion Recognition","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2105.01466","title":"GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts","created":"2021-05-04","cited_by_count":1,"area":null},{"id":"2105.01633","title":"An Estimation of Online Video User Engagement from Features of Continuous Emotions","created":"2021-05-04","cited_by_count":1,"area":null},{"id":"2106.11473","title":"Sequential Late Fusion Technique for Multi-modal Sentiment Analysis","created":"2021-06-22","cited_by_count":1,"area":null},{"id":"2312.12334","title":"PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis","created":"2023-12-19","cited_by_count":1,"area":null},{"id":"2507.13403","title":"UL-DD: A Multimodal Drowsiness Dataset Using Video, Biometric Signals, and Behavioral Data","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2404.04821","title":"A Data-to-Product Multimodal Conceptual Framework to Achieve Automated Software Evolution for Context-rich Intelligent Applications","created":"2024-04-07","cited_by_count":0,"area":null}],"next_cursor":null}