MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
cs.HC
/ Authors
Zheng Lian, Xiaojiang Peng, Kele Xu, Ziyu Jia, Xinyi Che, Zebang Cheng, Fei Ma, Laizhong Cui, Yazhou Zhang, Xin Liu
and 8 more authors
Liang Yang, Jia Li, Fan Zhang, Liumeng Xue, Erik Cambria, Guoying Zhao, Bjorn W. Schuller, Jianhua Tao
/ Abstract
MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent research trends, establishing itself as one of the largest challenges in the field. Throughout its history, the focus of MER has shifted from discriminative emotion recognition to generative emotion understanding. Specifically, MER2023 concentrated on discriminative emotion recognition, restricting the emotion recognition scope to fixed basic labels. In MER2024 and MER2025, we transitioned to generative emotion understanding and introduced two new tasks: fine-grained emotion recognition and descriptive emotion analysis, aiming to leverage the extensive vocabulary and multimodal understanding capabilities of Multimodal Large Language Models (MLLMs) to facilitate fine-grained and explainable emotion recognition. Building on this trajectory, MER2026 continues to follow these research trends and contains four tracks: MER-Cross shifts the focus from individual to dyadic interaction scenarios; MER-FG centers on fine-grained emotion recognition; MER-Prefer aims to predict human preferences regarding different emotion descriptions; MER-PS focuses on emotion recognition based on physiological signals. More details regarding the dataset and baselines are available at https://zeroqiaoba.github.io/MER-Challenge.