# HSEmotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognition

arXiv: https://arxiv.org/abs/2407.13184 · HTML: https://mlsanity.com/paper/2407.13184/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study](https://mlsanity.com/paper/2105.03790/) (2021)
- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Multi-Task Learning Framework for Emotion Recognition in-the-wild](https://mlsanity.com/paper/2207.09373/) (2022)
- [Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.09748/) (2022)
- [Affective Behaviour Analysis Using Pretrained Model with Facial Priori](https://mlsanity.com/paper/2207.11679/) (2022)
- [Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention](https://mlsanity.com/paper/2207.10293/) (2022)
- [Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge](https://mlsanity.com/paper/2207.10201/) (2022)
- [Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge](https://mlsanity.com/paper/2403.11450/) (2024)

## Built on by (most cited first, 6 of 6)

- [Affective Behaviour Analysis via Progressive Learning](https://mlsanity.com/paper/2407.16945/) (2024)
- [The Circumplex Degeneracy Behind the Rare-Class Limit in Affect Recognition](https://mlsanity.com/paper/2606.15763/) (2026)
- [7ABAW-Compound Expression Recognition via Curriculum Learning](https://mlsanity.com/paper/2503.07969/) (2025)
- [Bidirectional Learning of Facial Action Units and Expressions via Structured Semantic Mapping across Heterogeneous Datasets](https://mlsanity.com/paper/2604.10541/) (2026)
- [Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition](https://mlsanity.com/paper/2607.16290/) (2026)
- [AffectFuse: Cross-Task Feature Fusion with Temporal Modeling for Multi-Task Affective Behavior Analysis](https://mlsanity.com/paper/2607.16546/) (2026)

## Abstract (arXiv)

In this paper, we describe the results of the HSEmotion team in two tasks of the seventh Affective Behavior Analysis in-the-wild (ABAW) competition, namely, multi-task learning for simultaneous prediction of facial expression, valence, arousal, and detection of action units, and compound expression recognition. We propose an efficient pipeline based on frame-level facial feature extractors pre-trained in multi-task settings to estimate valence-arousal and basic facial expressions given a facial photo. We ensure the privacy-awareness of our techniques by using the lightweight architectures of neural networks, such as MT-EmotiDDAMFN, MT-EmotiEffNet, and MT-EmotiMobileFaceNet, that can run even on a mobile device without the need to send facial video to a remote server. It was demonstrated that a significant step in improving the overall accuracy is the smoothing of neural network output scores using Gaussian or box filters. It was experimentally demonstrated that such a simple post-processing of predictions from simple blending of two top visual models improves the F1-score of facial expression recognition up to 7%. At the same time, the mean Concordance Correlation Coefficient (CCC) of valence and arousal is increased by up to 1.25 times compared to each model's frame-level predictions. As a result, our final performance score on the validation set from the multi-task learning challenge is 4.5 times higher than the baseline (1.494 vs 0.32).