Multi-task Cross Attention Network in Facial Behavior Analysis

• URL: http://arxiv.org/abs/2207.10293v1
• Date: Thu, 21 Jul 2022 04:07:07 GMT
• Title: Multi-task Cross Attention Network in Facial Behavior Analysis
• Authors: Dang-Khanh Nguyen, Sudarshan Pant, Ngoc-Huynh Ho, Guee-Sang Lee, Soo-Huyng Kim, Hyung-Jeong Yang
• Abstract summary: We present our solution 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. We introduce a cross-attentive module to improve multi-task learning performance.
• Score: 7.910908058662372
• License: http://creativecommons.org/licenses/by/4.0/
• 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 open 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 1.24 on the validation data provided by the organizers, which is better than the baseline result of 0.30.

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