# 7ABAW-Compound Expression Recognition via Curriculum Learning

arXiv: https://arxiv.org/abs/2503.07969 · HTML: https://mlsanity.com/paper/2503.07969/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 59 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 30 of 35)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [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)
- [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)
- [Suppressing Uncertainties for Large-Scale Facial Expression Recognition](https://mlsanity.com/paper/2002.10392/) (2020)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://mlsanity.com/paper/2104.00232/) (2021)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition](https://mlsanity.com/paper/2207.10299/) (2022)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Facial expression and attributes recognition based on multi-task learning of lightweight neural networks](https://mlsanity.com/paper/2103.17107/) (2021)
- [EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation](https://mlsanity.com/paper/2305.18891/) (2023)
- [Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics](https://mlsanity.com/paper/2307.16620/) (2023)
- [BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge](https://mlsanity.com/paper/2308.10175/) (2023)
- [Multi-modal Facial Affective Analysis based on Masked Autoencoder](https://mlsanity.com/paper/2303.10849/) (2023)
- [Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement](https://mlsanity.com/paper/2303.01765/) (2023)
- [Affective Behaviour Analysis via Integrating Multi-Modal Knowledge](https://mlsanity.com/paper/2403.10825/) (2024)
- [Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks](https://mlsanity.com/paper/1705.07871/) (2017)
- [Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model](https://mlsanity.com/paper/1709.01664/) (2017)
- [Multi-Task Learning Framework for Emotion Recognition in-the-wild](https://mlsanity.com/paper/2207.09373/) (2022)
- [A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild](https://mlsanity.com/paper/2303.09293/) (2023)

## Abstract (arXiv)

With the advent of deep learning, expression recognition has made significant advancements. However, due to the limited availability of annotated compound expression datasets and the subtle variations of compound expressions, Compound Emotion Recognition (CE) still holds considerable potential for exploration. To advance this task, the 7th Affective Behavior Analysis in-the-wild (ABAW) competition introduces the Compound Expression Challenge based on C-EXPR-DB, a limited dataset without labels. In this paper, we present a curriculum learning-based framework that initially trains the model on single-expression tasks and subsequently incorporates multi-expression data. This design ensures that our model first masters the fundamental features of basic expressions before being exposed to the complexities of compound emotions. Specifically, our designs can be summarized as follows: 1) Single-Expression Pre-training: The model is first trained on datasets containing single expressions to learn the foundational facial features associated with basic emotions. 2) Dynamic Compound Expression Generation: Given the scarcity of annotated compound expression datasets, we employ CutMix and Mixup techniques on the original single-expression images to create hybrid images exhibiting characteristics of multiple basic emotions. 3) Incremental Multi-Expression Integration: After performing well on single-expression tasks, the model is progressively exposed to multi-expression data, allowing the model to adapt to the complexity and variability of compound expressions. The official results indicate that our method achieves the best performance in this competition track with an F-score of 0.6063. Our code is released at https://github.com/YenanLiu/ABAW7th.