Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data
Center for Machine Vision and Signal Analysis, University of Oulu（机器视觉与信号分析中心，奥卢大学）；Indian Institute of Technology Roorkee（印度理工学院罗尔基分校）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）
Puneet Kumar, Sarthak Malik, Balasubramanian Raman, Xiaobai Li
The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathetic, accurate, and engaging responses, with useful applications in education, healthcare, marketing, and customer service. To this end, we have constructed a large-scale Controllable Multimodal Feedback Synthesis (CMFeed) dataset and proposed a controllable feedback synthesis system. The system features an encoder, decoder, and controllability block for textual and visual inputs. It extracts features using a transformer and a Faster R-CNN network, combining them to generate feedback. The CMFeed dataset includes images, texts, reactions to the posts, human comments with relevance scores, and reactions to these comments. These reactions train the model to produce feedback with specified sentiments, achieving a sentiment classification accuracy of 77.23%, which is 18.82% higher than the accuracy without controllability. Access to the CMFeed dataset and the system's code is available at https://github.com/MIntelligence-Group/CMFeed.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Hebei University of Science and Technology（河北科技大学）
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
National University of Singapore（新加坡国立大学）；Wuhan University（武汉大学）；The University of Auckland（奥克兰大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）
The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress
University of Augsburg（奥格斯堡大学）；University of Ulm（乌尔姆大学）；Nanyang Technological University（南洋理工大学）；University of Oulu（奥卢大学）；Imperial College London（帝国理工学院）
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Henan University（河南大学）
Multimodal Aspect-Level Sentiment Analysis Based on Gated Noise Filtering and Emotion-Relevance Interaction
Hubei University（湖北大学）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（教育部智能感知系统与安全重点实验室（湖北大学））；Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北省大数据智能分析与应用重点实验室（湖北大学））
School of Computer Science and Engineering, NTU（南洋理工大学计算机科学与工程学院）；School of Natural Sciences, University of Stirling（斯特灵大学自然科学学院）；Temasek Laboratories, NTU（南洋理工大学淡马锡实验室）；National Institute of Technology, Warangal（瓦朗加尔国立理工学院）
TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
National University of Defense Technology（国防科技大学）；Hefei University of Technology（合肥工业大学）；Central China Normal University（华中师范大学）；Anhui Agricultural University（安徽农业大学）
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；University College London（伦敦大学学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Mixture of Disentangled Experts with Missing Modalities for Robust Multimodal Sentiment Analysis
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机学院）；School of Information Science and Technology, Beijing University of Technology（北京工业大学信息学部）
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；China University of Geosciences, Wuhan（中国地质大学（武汉））；Nanyang Technological University（南洋理工大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
Divide and Conquer: Mixture-of-Bottleneck Experts in Informative Ordinal Space for Video-based Multimodal Sentiment Analysis
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Electronics and Information Technology, Sun Yat-sen University（中山大学电子信息工程学院）；School of Cyberspace Security, Guangzhou University（广州大学网络空间安全学院）；School of Computer Science, South China Normal University（华南师范大学计算机学院）；VinUniversity（越南河内VinUniversity）；Nanyang Technological University（南洋理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Desay SV Automotive Co., Ltd（德赛西威汽车电子股份有限公司）
