MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Lian, Zheng; Sun, Haiyang; Sun, Licai; Wen, Zhuofan; Zhang, Siyuan; Chen, Shun; Gu, Hao; Zhao, Jinming; Ma, Ziyang; Chen, Xie; Yi, Jiangyan; Liu, Rui; Xu, Kele; Liu, Bin; Cambria, Erik; Zhao, Guoying; Schuller, Bjoern W.; Tao, Jianhua (2024-10-28)
ACM
28.10.2024
Lian, Z., Sun, H., Sun, L., Wen, Z., Zhang, S., Chen, S., Gu, H., Zhao, J., Ma, Z., Chen, X., Yi, J., Liu, R., Xu, K., Liu, B., Cambria, E., Zhao, G., Schuller, B. W., & Tao, J. (2024). Mer 2024: Semi-supervised learning, noise robustness, and open-vocabulary multimodal emotion recognition. Proceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing, 41–48. https://doi.org/10.1145/3689092.3689959
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412107144
https://urn.fi/URN:NBN:fi:oulu-202412107144
Tiivistelmä
Abstract
Multimodal emotion recognition is an important research topic in artificial intelligence. However, due to problems such as complex environments and inaccurate annotations, current systems are hard to meet the demands of practical applications. Therefore, we organize the MER series of competitions to promote the development of this field. Last year, we launched MER2023, focusing on three interesting topics: multi-label learning, noise robustness, and semi-supervised learning. In this year's MER2024, besides expanding the dataset size, we further introduce a new track around open-vocabulary emotion recognition. The main purpose of this track is that existing datasets usually fix the label space and use majority voting to enhance the annotator consistency. However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels. In this track, we encourage participants to generate any number of labels in any category, aiming to describe emotional states as accurately as possible. Our baseline code relies on MERTools and is available at: https://github.com/zeroQiaoba/MERTools/tree/master/MER2024.
Multimodal emotion recognition is an important research topic in artificial intelligence. However, due to problems such as complex environments and inaccurate annotations, current systems are hard to meet the demands of practical applications. Therefore, we organize the MER series of competitions to promote the development of this field. Last year, we launched MER2023, focusing on three interesting topics: multi-label learning, noise robustness, and semi-supervised learning. In this year's MER2024, besides expanding the dataset size, we further introduce a new track around open-vocabulary emotion recognition. The main purpose of this track is that existing datasets usually fix the label space and use majority voting to enhance the annotator consistency. However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels. In this track, we encourage participants to generate any number of labels in any category, aiming to describe emotional states as accurately as possible. Our baseline code relies on MERTools and is available at: https://github.com/zeroQiaoba/MERTools/tree/master/MER2024.
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