MCN-CL: Multimodal Cross-Attention Network and Contrastive Learning for Multimodal Emotion Recognition
School of Computer and Information Engineering, Anhui University of Finance and Economics, Anhui, 233030, China（计算机与信息工程学院，安徽财经大学，安徽，233030，中国）；State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100045, China（认知科学与心理健康国家重点实验室，心理学研究所，中国科学院，北京，100045，中国）
Feng Li, Ke Wu, Yongwei Li
备注：
Accepted by 32nd International Conference on MultiMedia Modeling (MMM 2026)
Multimodal emotion recognition plays a key role in many domains, including mental health monitoring, educational interaction, and human-computer interaction. However, existing methods often face three major challenges: unbalanced category distribution, the complexity of dynamic facial action unit time modeling, and the difficulty of feature fusion due to modal heterogeneity. With the explosive growth of multimodal data in social media scenarios, the need for building an efficient cross-modal fusion framework for emotion recognition is becoming increasingly urgent. To this end, this paper proposes Multimodal Cross-Attention Network and Contrastive Learning (MCN-CL) for multimodal emotion recognition. It uses a triple query mechanism and hard negative mining strategy to remove feature redundancy while preserving important emotional cues, effectively addressing the issues of modal heterogeneity and category imbalance. Experiment results on the IEMOCAP and MELD datasets show that our proposed method outperforms state-of-the-art approaches, with Weighted F1 scores improving by 3.42% and 5.73%, respectively.
University of Washington（华盛顿大学）；Microsoft Research Asia（微软亚洲研究院）
Self-supervised learning has been successfully applied to pre-train video representations, which aims at efficient adaptation from pre-training domain to downstream tasks. Existing approaches merely leverage contrastive loss to learn instance-level discrimination. However, lack of category information will lead to hard-positive problem that constrains the generalization ability of this kind of methods. We find that the multi-task process of meta learning can provide a solution to this problem. In this paper, we propose a Meta-Contrastive Network (MCN), which combines the contrastive learning a…
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）
Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC). Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers. In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task. Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch s…
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学（KAUST））
Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions. We observed a significant emotional bias towards instance-rich emotions, making trained neural speakers less accurate in describing under-represented emotions. We show that collecting new data, in the same way, is not effective in mitigating…
NaCTeM（国家文本挖掘中心）；The University of Manchester（曼彻斯特大学）；Umm Al-Qura University（乌姆·库拉大学）
A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Lear…
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省计算中心（国家超级计算济南中心）算力网络与信息安全教育部重点实验室）；Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science（山东省计算机网络重点实验室、山东省计算机科学基础研究中心）；Institute of Engineering Medicine, Beijing Institute of Technology（北京理工大学医学工程学院）
Beijing University of Technology（北京工业大学）；Beijing-Dublin International College（北京都柏林国际学院）
The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG…
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Multimodal Video Emotion Recognition with Reliable Reasoning Priors
Lenovo Research（联想研究院）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Institute of Automation, CAS（中国科学院自动化研究所）；Macau University of Science and Technology（澳门科学理工学院）；School of Computer Science and Technology, UCAS（中国科学院大学计算机科学与技术学院）
Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学生物医学工程学院深圳校区）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
Human beings have rich ways of emotional expressions, including facial action, voice, and natural languages. Due to the diversity and complexity of different individuals, the emotions expressed by various modalities may be semantically irrelevant. Directly fusing information from different modalities may inevitably make the model subject to the noise from semantically irrelevant modalities. To tackle this problem, we propose a multimodal relevance estimation network to capture the relevant semantics among modalities in multimodal emotions. Specifically, we take advantage of an attention mechan…
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby d…
UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥国家综合科学中心人工智能研究所）；Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines, School of Computer Science and Information Engineering, Hefei University of Technology（安徽省情感计算与先进智能机器重点实验室，合肥工业大学计算机科学与信息工程学院）；Department of Computer and Network Engineering, The University of Electro-Communications（电子通信大学计算机与网络工程系）
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Laboratory for Big Data and Decision, School of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院大数据与决策实验室）
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion
School of Computer Science and Engineering, Sun Yat-Sen University（中山大学计算机科学与工程学院）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与先进计算重点实验室）
CTSM: Combining Trait and State Emotions for Empathetic Response Model
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Digital Fujian Institute of Financial Big Data（数字福建金融大数据研究院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications that require discriminative representation of consistent attributes within an utterance: speaker, language, emotion, and age. Existing frame-level self-supervised speech representation, e.g., wav2vec, can be used as utterance-level representation with pooling, but the models are usually large. There are also SSL techniques to learn utterance-level rep…
DRKF: Decoupled Representations with Knowledge Fusion for Multimodal Emotion Recognition
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
College of Computer and Mathematics（计算机与数学学院）；Central South University of Forestry and Technology（林业科技大学）；Changsha Hospital for Maternal and Child Health Care（长沙妇幼保健医院）；Department of Computer Science, State University of New York（纽约州立大学计算机科学系）
Cross-domain EEG-based Emotion Recognition with Contrastive Learning
School of Software Engineering, Xi'an Jiaotong University, China（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi'an Jiaotong University, China（西安交通大学计算机科学与技术学院）
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心Eurecat）；University of Trento（特伦托大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
Emotion recognition is involved in several real-world applications. With an increase in available modalities, automatic understanding of emotions is being performed more accurately. The success in Multimodal Emotion Recognition (MER), primarily relies on the supervised learning paradigm. However, data annotation is expensive, time-consuming, and as emotion expression and perception depends on several factors (e.g., age, gender, culture) obtaining labels with a high reliability is hard. Motivated by these, we focus on unsupervised feature learning for MER. We consider discrete emotions, and as …
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition
School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology（仪器科学与光电工程学院，合肥工业大学）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semanti…
Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究中心）；University of Trieste（的里雅斯特大学）；Leiden University Medical Center（莱顿大学医学中心）；Delft University of Technology（代尔夫特理工大学）
Emotion recognition in children can help the early identification of, and intervention on, psychological complications that arise in stressful situations such as cancer treatment. Though deep learning models are increasingly being adopted, data scarcity is often an issue in pediatric medicine, including for facial emotion recognition in children. In this paper, we study the application of data augmentation-based contrastive learning to overcome data scarcity in facial emotion recognition for children. We explore the idea of ignoring generational gaps, by adding abundantly available adult data …
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Speech emotion recognition is an important aspect of human-computer interaction. Prior work proposes various end-to-end models to improve the classification performance. However, most of them rely on the cross-entropy loss together with softmax as the supervision component, which does not explicitly encourage discriminative learning of features. In this paper, we introduce the contrastive loss function to encourage intra-class compactness and inter-class separability between learnable features. Furthermore, multiple feature selection methods and pairwise sample selection methods are evaluated.…
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Automation, Tsinghua University（清华大学自动化系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition
