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）
M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.
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）
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）
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis
Information Networking Institute at Carnegie Mellon University (CMU)（卡内基梅隆大学信息网络研究所）；School of Computer Science and Technology at Tongji University（同济大学计算机科学与技术学院）
Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention
Fraunhofer IAIS - Department of Media Engineering（弗劳恩霍夫研究所媒体工程部门）；University of Bonn - Department of Computer Science（波恩大学计算机科学系）；West-AI - Federal Ministry of Education and Research（西德人工智能 - 教育与研究部）
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）
Variational Fusion for Multimodal Sentiment Analysis
A*STAR AI Initiative, Institute of High Performance Computing（A*STAR人工智能计划、高性能计算研究院）；Adobe Research（奥多比研究院）；University of Michigan（密歇根大学）；Centro de Investigación en Computación, Instituto Politécnico Nacional（国立理工学院计算研究中心）；SUTD（新加坡科技设计大学）
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）
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）
ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors
Southwest Jiaotong University（西南交通大学）；Microsoft Research Asia（微软亚洲研究院）；Southwestern University of Finance and Economics（西南财经大学）；Ocean University of China（中国海洋大学）；Tencent（腾讯）
FedUAF: Uncertainty-Aware Fusion with Reliability-Guided Aggregation for Multimodal Federated Sentiment Analysis
Shanghai University（上海大学）；North China Electric Power University（华北电力大学）；Université de Technologie de Compiègne（法国图卢兹国立理工学院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Macquarie University（麦考瑞大学）
MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image
College of Computer, National University of Defense Technology（国防科技大学计算机学院）；School of Computer and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；CCNU（中国地质大学）
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Singapore University of Social Sciences, Singapore（新加坡社会科学研究大学）
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；Academy of Cyber（网络空间研究院）
Target-oriented Multimodal Sentiment Classification with Counterfactual-enhanced Debiasing
School of Computer, Electronics and Information（计算机、电子与信息学院）；Guangxi University（广西大学）；Guangxi Key Laboratory of Multimedia Communications（广西多媒体通信与网络技术重点实验室）；School of Sociology and Anthropology（社会学与人类学学院）；Sun Yat-sen University（中山大学）
