Expression recognition in in-the-wild video data remains challenging due to substantial variations in facial appearance, background conditions, audio noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient for capturing these complex emotional cues. To address this limitation, we propose a multimodal emotion recognition framework for the Expression (EXPR) task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge. Our framework builds on large-scale pre-trained models for visual and audio representation learning and integrates them in a unified multimodal architecture. To better capture temporal patterns in facial expression sequences, we incorporate temporal visual modeling over video windows. We further introduce a bi-directional cross-attention fusion module that enables visual and audio features to interact in a symmetric manner, facilitating cross-modal contextualization and complementary emotion understanding. In addition, we employ a text-guided contrastive objective to encourage semantically meaningful visual representations through alignment with emotion-related text prompts. Experimental results on the ABAW 10th EXPR benchmark demonstrate the effectiveness of the proposed framework, achieving a Macro F1 score of 0.32 compared to the baseline score of 0.25, and highlight the benefit of combining temporal visual modeling, audio representation learning, and cross-modal fusion for robust emotion recognition in unconstrained real-world environments.
University of Nantong（南通大学）；Nanjing University of Science and Technology（南京理工大学）
Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our…
Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding
Shenzhen Technology University（深圳技术大学）；Shenzhen University of Information Technology（深圳信息科技学院）；University of Washington（华盛顿大学）；Foundation Model Team, Meituan（美团基础模型团队）；National University of Singapore（新加坡国立大学）；Tongji University（同济大学）
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…
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）
Automatic emotion recognition is a challenging task. In this paper, we present our effort for the audio-video based sub-challenge of the Emotion Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to assign a single emotion label to the video clip from the six universal emotions (Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed multimodal emotion recognition system takes audio, video and text information into account. Except for handcraft features, we also extract bottleneck features from deep neutral networks (DNNs) via transfer learning. B…
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）
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
School of Software（软件学院）；Dalian University of Technology（大连理工大学）；School of Foreign Languages and School of Software（外语学院和软件学院）；Faculty of Business Administration（商学院）；University of Macau（澳门大学）；School of Computing Technologies（计算技术学院）；RMIT University（皇家墨尔本理工大学）
RoHyDR: Robust Hybrid Diffusion Recovery for Incomplete Multimodal Emotion Recognition
School of Computer Science and Engineering（计算机科学与工程学院）；South China University of Technology（华南理工大学）；School of Future Technology（未来技术学院）；University of Oxford（牛津大学）；Pengcheng Laboratory（鹏城实验室）；Department of Computer Science（计算机科学系）
XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning
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）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第五十四研究所）
Renmin University of China（中国人民大学）；National University of Singapore（新加坡国立大学）
Multimodal emotion recognition study is hindered by the lack of labelled corpora in terms of scale and diversity, due to the high annotation cost and label ambiguity. In this paper, we propose a pre-training model \textbf{MEmoBERT} for multimodal emotion recognition, which learns multimodal joint representations through self-supervised learning from large-scale unlabeled video data that come in sheer volume. Furthermore, unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text predicti…
Dynamic Fusion-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition in Conversations
College of Computer and Mathematics, Central South University of Forestry and Technology（林业科技大学计算机与数学学院）；Department of Computer Science, State University of New York, New Paltz, New York, 12561, USA（纽约州立大学新帕尔兹分校计算机科学系）
In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge. However, the studies on rumor detection ignore the intense emotions of images in the rumor content. This paper verifies that the image emotion improves the rumor detection efficiency. A Multimodal Dual Emotion feature in rumor detection, which consists of visual and textual emotions, is proposed. To the best of our knowledge, this is the first study which uses visual emotion in rumor detection. The experiments on real datasets verify that the proposed features outperform the state-…
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition
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）
Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person's emotions are influenced by the other speaker's utterances and their own emotional state over the utterances. In this paper, we propose COntextualized Graph Neural Network based Multimodal Emotion recognitioN (COGMEN) system that leverages local information (i.e., inter/intra dependency between speakers) and global information (context). The proposed model uses Graph Neural Network (…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
This paper proposes a multimodal emotion recognition system based on hybrid fusion that classifies the emotions depicted by speech utterances and corresponding images into discrete classes. A new interpretability technique has been developed to identify the important speech & image features leading to the prediction of particular emotion classes. The proposed system's architecture has been determined through intensive ablation studies. It fuses the speech & image features and then combines speech, image, and intermediate fusion outputs. The proposed interpretability technique incorporates the …
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition
Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Centrale Lille（里尔中央理工学院）；Institut Mines-Télécom（法国国立高等矿业电信学校联盟）；IMT Nord Europe（法国国立高等矿业电信学校联盟北欧分校）；INSERM（法国国家健康与医学研究院）；University of Lille（里尔大学）
The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions. In this paper, we present our works on arousal and valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal representations are first extracted from videos using a variety of acoustic, video and textual models and support vector machine (SVM) is then used for fusion of multimodal signals to make final predictions. Our solution achieves Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal and valence respectively for the validation dataset, wh…
EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agent Specialization
Guangdong University of Technology（广东工业大学）；Southern University of Science and Technology（南方科技大学）；Xi’an Jiaotong University（西安交通大学）；Guizhou Provincial Laboratory of Big Data, State Key Laboratory of Public Big Data, Guizhou University（贵州大学大数据省级重点实验室、公共大数据国家重点实验室）
Numerous studies have shown that machine learning algorithms can latch onto protected attributes such as race and gender and generate predictions that systematically discriminate against one or more groups. To date the majority of bias and fairness research has been on unimodal models. In this work, we explore the biases that exist in emotion recognition systems in relationship to the modalities utilized, and study how multimodal approaches affect system bias and fairness. We consider audio, text, and video modalities, as well as all possible multimodal combinations of those, and find that tex…
Emotion recognition is a core research area at the intersection of artificial intelligence and human communication analysis. It is a significant technical challenge since humans display their emotions through complex idiosyncratic combinations of the language, visual and acoustic modalities. In contrast to traditional multimodal fusion techniques, we approach emotion recognition from both direct person-independent and relative person-dependent perspectives. The direct person-independent perspective follows the conventional emotion recognition approach which directly infers absolute emotion lab…
Deakin University（迪肯大学）；University of Sydney（悉尼大学）；University of Tasmania（塔斯马尼亚大学）；Queensland University of Technology（昆士兰科技大学）
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet representative features from multimodal data, (ii) how to effectively capture complementary features from multimodal streams, and (iii) how to perform all the tasks in an end-to-end manner. To address these challenges, in this paper, we propose a nove…
Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark
Hefei University of Technology（合肥工业大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence (IAI), Hefei Comprehensive National Science Center（人工智能研究院（IAI），合肥综合性国家科学中心）
Noise-Resistant Multimodal Transformer for Emotion Recognition
China University of Geosciences (Wuhan)（中国地质大学（武汉））；JD Explore Academy, JD.com（京东集团京东探索研究院）；Australian Catholic University（澳大利亚天主教大学）；The University of Sydney（悉尼大学）
In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users' inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimoda…
EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models
Lappeenranta-Lahti University of Technology LUT（拉佩兰塔-拉赫蒂技术大学）；University of Oulu（奥卢大学）；Southeast University（东南大学）；Shanghai Jiao Tong University（上海交通大学）；Brno University of Technology（布拉格技术大学）
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
Tianjin University（天津大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
