The 2nd Challenge on Micro-gesture Analysis for Hidden Emotion Understanding (MiGA) 2024: Dataset and Results
Chen, Haoyu; Schuller, Björn W.; Adeli, Ehsan; Zhao, Guoying (2024-08-04)
RWTH Aachen
04.08.2024
Chen, Haoyu; Schuller, Björn W.; Adeli, Ehsan; Zhao, Guoying (2024) The 2nd Challenge on Micro-gesture Analysis for Hidden Emotion Understanding (MiGA) 2024: Dataset and Results. In MiGA 2024: Proceedings of IJCAI 2024 Workshop&Challenge on Micro-gesture Analysis for Hidden Emotion Understanding (MiGA 2024) co-located with 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), CEUR workshop proceedings, 3848, 1-9, https://ceur-ws.org/Vol-3848/paper_7.pdf
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© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202501131138
https://urn.fi/URN:NBN:fi:oulu-202501131138
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Abstract:
This paper summarizes the 2nd Challenge of Microgesture Analysis for Hidden Emotion Understanding (MiGA) 2024. The competition was split into two independent tracks: micro-gesture classification from pre-segmented data clips, and micro-gesture online recognition in sequences of continuous data. In this edition of the MiGA challenge, both tracks use multi-modal data (RGB and skeleton as modalities). For evaluation, accuracy for classification and F1 score for online recognition are used as the evaluation measure. Two large micro-gesture datasets (iMiGUE and SMG) were made publicly available and the Kaggle platform was used to manage the competition. Results achieved a classification accuracy of 70.25% for micro-gesture classification, showing a significant improvement compared to last year’s competition, meanwhile, an F1 score for online recognition is about 0.2757 was achieved for multi-modal gesture recognition, showing the task is still challenging and leaves considerable margin for improvement.
This paper summarizes the 2nd Challenge of Microgesture Analysis for Hidden Emotion Understanding (MiGA) 2024. The competition was split into two independent tracks: micro-gesture classification from pre-segmented data clips, and micro-gesture online recognition in sequences of continuous data. In this edition of the MiGA challenge, both tracks use multi-modal data (RGB and skeleton as modalities). For evaluation, accuracy for classification and F1 score for online recognition are used as the evaluation measure. Two large micro-gesture datasets (iMiGUE and SMG) were made publicly available and the Kaggle platform was used to manage the competition. Results achieved a classification accuracy of 70.25% for micro-gesture classification, showing a significant improvement compared to last year’s competition, meanwhile, an F1 score for online recognition is about 0.2757 was achieved for multi-modal gesture recognition, showing the task is still challenging and leaves considerable margin for improvement.
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