# 7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition

arXiv: https://arxiv.org/abs/2407.03835 · HTML: https://mlsanity.com/paper/2407.03835/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 100 of its 128 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 30 of 100)

- [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: 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)
- [Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond](https://mlsanity.com/paper/2401.01219/) (2024)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [Transformer-based Multimodal Information Fusion for Facial Expression Analysis](https://mlsanity.com/paper/2203.12367/) (2022)
- [Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset](https://mlsanity.com/paper/1910.01417/) (2019)
- [Multitask Emotion Recognition with Incomplete Labels](https://mlsanity.com/paper/2002.03557/) (2020)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2203.14779/) (2022)
- [Photorealistic Facial Synthesis in the Dimensional Affect Space](https://mlsanity.com/paper/1811.08004/) (2018)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans](https://mlsanity.com/paper/2403.02192/) (2024)
- [A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition](https://mlsanity.com/paper/2107.04187/) (2021)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm](https://mlsanity.com/paper/1809.04359/) (2018)
- [Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3](https://mlsanity.com/paper/2203.13031/) (2022)
- [BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2310.03485/) (2023)
- [A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis](https://mlsanity.com/paper/2107.04225/) (2021)
- [Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition](https://mlsanity.com/paper/2107.03708/) (2021)
- [Affective Behaviour Analysis via Integrating Multi-Modal Knowledge](https://mlsanity.com/paper/2403.10825/) (2024)
- [Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition](https://mlsanity.com/paper/2203.13052/) (2022)
- [Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition](https://mlsanity.com/paper/1707.05395/) (2017)

## Built on by (most cited first, 30 of 53)

- [Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit](https://mlsanity.com/paper/2409.17717/) (2024)
- [HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition](https://mlsanity.com/paper/2503.10399/) (2025)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios](https://mlsanity.com/paper/2506.05372/) (2025)
- [Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild](https://mlsanity.com/paper/2407.12927/) (2024)
- [Affective Behavior Analysis using Task-adaptive and AU-assisted Graph Network](https://mlsanity.com/paper/2407.11663/) (2024)
- [Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challenge](https://mlsanity.com/paper/2407.12390/) (2024)
- [MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network](https://mlsanity.com/paper/2503.12623/) (2025)
- [Affective Behaviour Analysis via Progressive Learning](https://mlsanity.com/paper/2407.16945/) (2024)
- [Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency](https://mlsanity.com/paper/2408.02164/) (2024)
- [HSEmotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognition](https://mlsanity.com/paper/2407.13184/) (2024)
- [Ig3D: Integrating 3D Face Representations in Facial Expression Inference](https://mlsanity.com/paper/2408.16907/) (2024)
- [Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style](https://mlsanity.com/paper/2410.03848/) (2024)
- [Emotion Recognition with CLIP and Sequential Learning](https://mlsanity.com/paper/2503.09929/) (2025)
- [Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space](https://mlsanity.com/paper/2503.10104/) (2025)
- [Semantic Matters: Multimodal Features for Affective Analysis](https://mlsanity.com/paper/2504.11460/) (2025)
- [Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensation](https://mlsanity.com/paper/2407.15798/) (2024)
- [Compound Expression Recognition via Large Vision-Language Models](https://mlsanity.com/paper/2503.11241/) (2025)
- [Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challenge](https://mlsanity.com/paper/2407.12258/) (2024)
- [Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition](https://mlsanity.com/paper/2503.10603/) (2025)
- [FIESTA: Fisher Information-based Efficient Selective Test-time Adaptation](https://mlsanity.com/paper/2503.23257/) (2025)
- [Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track](https://mlsanity.com/paper/2603.13760/) (2026)
- [Compound Expression Recognition via Multi Model Ensemble for the ABAW7 Challenge](https://mlsanity.com/paper/2407.12257/) (2024)
- [MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing](https://mlsanity.com/paper/2409.07256/) (2024)
- [Interactive Multimodal Fusion with Temporal Modeling](https://mlsanity.com/paper/2503.10523/) (2025)
- [V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition](https://mlsanity.com/paper/2503.15970/) (2025)
- [Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach](https://mlsanity.com/paper/2507.02205/) (2025)
- [Grounding Emotion Recognition with Visual Prototypes: VEGA -- Revisiting CLIP in MERC](https://mlsanity.com/paper/2508.06564/) (2025)
- [Solution to the 10th ABAW Expression Recognition Challenge: A Robust Multimodal Framework with Safe Cross-Attention and Modality Dropout](https://mlsanity.com/paper/2603.08034/) (2026)
- [Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild](https://mlsanity.com/paper/2603.11306/) (2026)

## Abstract (arXiv)

This paper describes the 7th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ECCV 2024. The 7th ABAW Competition addresses novel challenges in understanding human expressions and behaviors, crucial for the development of human-centered technologies. The Competition comprises of two sub-challenges: i) Multi-Task Learning (the goal is to learn at the same time, in a multi-task learning setting, to estimate two continuous affect dimensions, valence and arousal, to recognise between the mutually exclusive classes of the 7 basic expressions and 'other'), and to detect 12 Action Units); and ii) Compound Expression Recognition (the target is to recognise between the 7 mutually exclusive compound expression classes). s-Aff-Wild2, which is a static version of the A/V Aff-Wild2 database and contains annotations for valence-arousal, expressions and Action Units, is utilized for the purposes of the Multi-Task Learning Challenge; a part of C-EXPR-DB, which is an A/V in-the-wild database with compound expression annotations, is utilized for the purposes of the Compound Expression Recognition Challenge. In this paper, we introduce the two challenges, detailing their datasets and the protocols followed for each. We also outline the evaluation metrics, and highlight the baseline systems and their results. Additional information about the competition can be found at urlhttps://affective-behavior-analysis-in-the-wild.github.io/7th.