Affective Behavior Analysis Using Action Unit Relation Graph and Multi-Task Cross Attention
Abstract
Facial behavior analysis is a broad topic with various categories such as facial emotion recognition, age, and gender recognition. Many studies focus on individual tasks while the multi-task learning approach is still an open research issue and requires more research. In this paper, we present our solution and experiment result for the Multi-Task Learning challenge of the Affective Behavior Analysis in-the-wild competition. The challenge is a combination of three tasks: action unit detection, facial expression recognition, and valance-arousal estimation. To address this challenge, we introduce a cross-attentive module to improve multi-task learning performance. Additionally, a facial graph is applied to capture the association among action units. As a result, we achieve the evaluation measure of 128.8 on the validation data provided by the organizers, which outperforms the baseline result of 30.
Cite
Text
Nguyen et al. "Affective Behavior Analysis Using Action Unit Relation Graph and Multi-Task Cross Attention." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25075-0_10Markdown
[Nguyen et al. "Affective Behavior Analysis Using Action Unit Relation Graph and Multi-Task Cross Attention." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/nguyen2022eccvw-affective/) doi:10.1007/978-3-031-25075-0_10BibTeX
@inproceedings{nguyen2022eccvw-affective,
title = {{Affective Behavior Analysis Using Action Unit Relation Graph and Multi-Task Cross Attention}},
author = {Nguyen, Dang-Khanh and Pant, Sudarshan and Ho, Ngoc-Huynh and Lee, Guee-Sang and Kim, Soo-Hyung and Yang, Hyung-Jeong},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {132-142},
doi = {10.1007/978-3-031-25075-0_10},
url = {https://mlanthology.org/eccvw/2022/nguyen2022eccvw-affective/}
}