TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning

Pengcheng Wang, Zihao Wang, Zhilong Ji, Xiao Liu, Songfan Yang, Zhongqin Wu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 412-413

Abstract

This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately. The co-occurrence of the expression features and the head pose features are explored. We observe that different AUs converge at various speed. By choosing the optimal checkpoint for each AU, the recognition results are improved. We are able to obtain a final score of 0.746 in validation set and 0.7306 in the test set of the challenge.

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[bibtex]
@InProceedings{Wang_2020_CVPR_Workshops,
author = {Wang, Pengcheng and Wang, Zihao and Ji, Zhilong and Liu, Xiao and Yang, Songfan and Wu, Zhongqin},
title = {TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}