MER 2025: When Affective Computing Meets Large Language Models
Year: 2025
Type: conference-paper
Abstract: MER2025 is the third year of our MER series of challenges. Previously, MER2023 (http://merchallenge.cn/mer2023) focused on multi-label learning, noise robustness, and semi-supervised learning, while MER2024 (https://zeroqiaoba.github.io/MER2024-website) introduced a new track dedicated to open-vocabulary emotion recognition. This year, MER2025 centers on the theme ''When Affective Computing Meets Large Language Models (LLMs)''. We aim to shift the paradigm from traditional categorical frameworks reliant on predefined emotion taxonomies to LLM-driven generative methods, offering innovative solutions for more accurate and reliable emotion understanding. The challenge contains four tracks: MER-SEMI focuses on fixed categorical emotion recognition enhanced by semi-supervised learning; MER-FG explores fine-grained emotions, expanding recognition from basic to nuanced emotional states; MER-DES incorporates multimodal cues (beyond emotion words) into predictions to enhance model interpretability; MER-PR reveals whether emotion prediction results can improve personality recognition performance. For the first three tracks, the baseline code is available at MERTools (https://github.com/zeroQiaoba/MERTools) and datasets can be accessed via Hugging Face (https://huggingface.co/datasets/MERChallenge/MER2025). For the last track, the dataset and baseline code are available on GitHub (https://github.com/cai-cong/MER25_personality).
Authors: Zheng Lian, Rui Liu, Kele Xu, Bin Liu, Xuefei Liu, Yazhou Zhang, Xin Liu, Yong Li, Zebang Cheng, Haolin Zuo, Ziyang Ma, Xiaojiang Peng, Xie Chen, Ya Li, Erik Cambria, Guoying Zhao, Björn W. Schuller, Jianhua Tao
Institutions: Chinese Academy of Sciences, Institute of Automation, Inner Mongolia University, National University of Defense Technology, Tianjin Normal University, Lappeenranta-Lahti University of Technology, Tianjin University, Beijing University of Posts and Telecommunications, Southeast University, Shenzhen University, Shanghai Jiao Tong University, Shenzhen Technology University, Nanyang Technological University, University of Oulu, Technical University of Munich, Tsinghua University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G3204720597
OpenAlex ID: W4415538029