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.

翻译：MER2026是MER系列挑战赛的第四届。MER系列为研究社区提供了宝贵的数据资源，并围绕近期研究趋势设置任务，已成为该领域规模最大的挑战赛之一。在其发展历程中，MER的关注点已从判别式情感识别转向生成式情感理解。具体而言，MER2023专注于判别式情感识别，将情感识别范围限制在固定的基本标签上。在MER2024和MER2025中，我们转向生成式情感理解，并引入两项新任务：细粒度情感识别和描述性情感分析，旨在利用多模态大语言模型（MLLMs）的丰富词汇和多模态理解能力，促进可解释的细粒度情感识别。基于这一发展路径，MER2026延续了上述研究趋势，包含四个赛道：MER-Cross将关注点从个体转向两人交互场景；MER-FG聚焦于细粒度情感识别；MER-Prefer旨在预测人类对不同情感描述的偏好；MER-PS专注于基于生理信号的情感识别。有关数据集和基线的更多详情，请访问https://zeroqiaoba.github.io/MER-Challenge。