Summary of Mer 2024: Semi-supervised Learning, Noise Robustness, and Open-vocabulary Multimodal Emotion Recognition, by Zheng Lian et al.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
by Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen, Siyuan Zhang, Shun Chen, Hao Gu, Jinming Zhao, Ziyang Ma, Xie Chen, Jiangyan Yi, Rui Liu, Kele Xu, Bin Liu, Erik Cambria, Guoying Zhao, Björn W. Schuller, Jianhua Tao
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Multimodal Emotion Recognition (MER) series of competitions aims to advance the development of artificial intelligence in recognizing emotions. The previous year’s MER2023 focused on multi-label learning, noise robustness, and semi-supervised learning. This year’s MER2024 introduces a new track on open-vocabulary emotion recognition, where participants are encouraged to generate labels in any category to describe emotional states accurately. The goal is to improve the accuracy of emotion recognition by moving beyond traditional majority voting-based annotation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MER series aims to make artificial intelligence better at recognizing emotions. To do this, they’re holding competitions and encouraging people to come up with new ways to recognize emotions. Last year’s competition focused on making systems work in different situations and using a mix of labeled and unlabeled data. This year, they’re adding a new challenge where participants can choose their own labels to describe emotional states. The goal is to make emotion recognition more accurate by not relying solely on majority votes. |
Keywords
» Artificial intelligence » Semi supervised
