MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| author | Lian, Zheng Peng, Xiaojiang Xu, Kele Jia, Ziyu Che, Xinyi Cheng, Zebang Ma, Fei Cui, Laizhong Zhang, Yazhou Liu, Xin Yang, Liang Li, Jia Zhang, Fan Xue, Liumeng Cambria, Erik Zhao, Guoying Schuller, Bjorn W. Tao, Jianhua |
| author_facet | Lian, Zheng Peng, Xiaojiang Xu, Kele Jia, Ziyu Che, Xinyi Cheng, Zebang Ma, Fei Cui, Laizhong Zhang, Yazhou Liu, Xin Yang, Liang Li, Jia Zhang, Fan Xue, Liumeng Cambria, Erik Zhao, Guoying Schuller, Bjorn W. Tao, Jianhua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19417 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding Lian, Zheng Peng, Xiaojiang Xu, Kele Jia, Ziyu Che, Xinyi Cheng, Zebang Ma, Fei Cui, Laizhong Zhang, Yazhou Liu, Xin Yang, Liang Li, Jia Zhang, Fan Xue, Liumeng Cambria, Erik Zhao, Guoying Schuller, Bjorn W. Tao, Jianhua Human-Computer Interaction 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. |
| title | MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.19417 |