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

arXiv: https://arxiv.org/abs/2004.09862 · HTML: https://mlsanity.com/paper/2004.09862/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Affective Behaviour Analysis Using Pretrained Model with Facial Priori](https://mlsanity.com/paper/2207.11679/) (2022)
- [Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency](https://mlsanity.com/paper/2408.02164/) (2024)

## Abstract (arXiv)

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.