Micro-gesture Classification Based on Ensemble Hypergraph-convolution Transformer

Hexiang Huang, Xu Peng Guo, Wei Peng, Zhaoqiang Xia

科研成果: 期刊稿件会议文章同行评审

2 引用 (Scopus)

摘要

Micro-gesture classification has emerged as a significant research area within emotion analysis and human-computer interaction, garnering increasing attention. While some skeleton-based action recognition algorithms utilizing graph convolution networks have shown competence in micro-gesture classification, these deep models still face challenges in representing subtle temporal actions and handling the long-tailed distribution of samples. To address these issues, this paper proposes a deep framework with ensemble hypergraph-convolution Transformers, which fuses multiple models focused on various categories. In this model, the Transformers with hypergraph based attention are constructed and extended to enhance the representation ability of single model. Then a data grouping training and ensemble method is employed to handle imbalanced categories for micro-gestures, resulting in a significant improvement in classification accuracy of single models. Finally, our algorithm model is evaluated on the iMiGUE dataset, which achieves the Top-1 accuracy of 0.6302 and the second ranking in the MiGA2023 Challenge (Track 1: Micro-gesture Classification).

源语言英语
期刊CEUR Workshop Proceedings
3522
出版状态已出版 - 2023
活动1st IJCAI Workshop and Challenge on Micro-Gesture Analysis for Hidden Emotion Understanding, MiGA 2023 - Macau, 中国
期限: 21 8月 202322 8月 2023

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