MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition

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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Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1145/3689092.3689959
Citations
21
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
10.66
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article

MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition

Zheng Lian, Shun Chen, Siyuan Zhang, Kele Xu et al.
21 citations
Emotion and Mood Recognition
10.66
article

MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition

Zheng Lian, Shun Chen, Siyuan Zhang, Kele Xu, Licai Sun, Björn W. Schuller, Bin Liu, Haiyang Sun, Jiangyan Yi, Guoying Zhao, Ziyang Ma, Rui Liu, Jinming Zhao, Hao Gu, Jianhua Tao, Erik Cambria, Xie Chen, Zhuofan Wen
article en
21 citations

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.

National University of Defense Technology (CN), Nanyang Technological University (SG), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Inner Mongolia University (CN), Institute of Automation (CN), Imperial College London (GB), Renmin University of China (CN), University of Oulu (FI), Tsinghua University (CN)
Quality Education
Openalex Percentile: Top 1%
Emotion and Mood Recognition
10.66
DOIOpen Access PDFOpenAlex
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