Multi Model Ensemble for Compound Expression Recognition
Abstract
Compound Expression Recognition (CER) plays a crucial role in interpersonal interactions. Due to the complexity of human emotional expressions, which leads to the existence of compound expressions, it is necessary to consider both local and global facial expressions comprehensively for recognition. In this paper, to address this issue, we propose a solution for compound expression recognition based on ensemble learning methods. Specifically, our task is classification. We trained three expression classification models based on convolutional networks (ResNet50), Vision Transformers, and multi-scale local attention networks, respectively. Then, by using late fusion, integrated the outputs of three models to predict the final result, leveraging the strengths of different models. Our method achieves high accuracy on RAF-DB and in sixth Affective Behavior Analysis in-the-wild (ABAW) Challenge, achieves an F1 score of 0.224 on the test set of C-EXPR-DB.
Cite
Text
Yu et al. "Multi Model Ensemble for Compound Expression Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00491Markdown
[Yu et al. "Multi Model Ensemble for Compound Expression Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/yu2024cvprw-multi/) doi:10.1109/CVPRW63382.2024.00491BibTeX
@inproceedings{yu2024cvprw-multi,
title = {{Multi Model Ensemble for Compound Expression Recognition}},
author = {Yu, Jun and Zhu, Jichao and Zhu, Wangyuan and Cai, Zhongpeng and Zhao, Gongpeng and Wei, Zhihong and Xie, Guochen and Zhang, Zerui and Liu, Qingsong and Liang, Jiaen},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {4873-4879},
doi = {10.1109/CVPRW63382.2024.00491},
url = {https://mlanthology.org/cvprw/2024/yu2024cvprw-multi/}
}