# ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges

arXiv: https://arxiv.org/abs/2202.10659 · HTML: https://mlsanity.com/paper/2202.10659/

## Key facts

- Cited by 156 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 89 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 30 of 38)

- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [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)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition](https://mlsanity.com/paper/1811.07770/) (2018)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [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)
- [Photorealistic Facial Synthesis in the Dimensional Affect Space](https://mlsanity.com/paper/1811.08004/) (2018)
- [A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions](https://mlsanity.com/paper/1811.07771/) (2018)
- [A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition](https://mlsanity.com/paper/2107.04187/) (2021)
- [Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm](https://mlsanity.com/paper/1809.04359/) (2018)
- [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)
- [Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition](https://mlsanity.com/paper/1707.05395/) (2017)
- [A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild](https://mlsanity.com/paper/1805.01452/) (2018)
- [M3T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild](https://mlsanity.com/paper/2002.02957/) (2020)
- [Affect Expression Behaviour Analysis in the Wild using Spatio-Channel Attention and Complementary Context Information](https://mlsanity.com/paper/2009.14440/) (2020)
- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [Multitask Multi-database Emotion Recognition](https://mlsanity.com/paper/2107.04127/) (2021)
- [Emotion Recognition for In-the-wild Videos](https://mlsanity.com/paper/2002.05447/) (2020)
- [Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model](https://mlsanity.com/paper/2002.09120/) (2020)
- [A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wild](https://mlsanity.com/paper/2009.13885/) (2020)

## Built on by (most cited first, 30 of 156)

- [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)
- [Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond](https://mlsanity.com/paper/2401.01219/) (2024)
- [7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition](https://mlsanity.com/paper/2407.03835/) (2024)
- [Transformer-based Multimodal Information Fusion for Facial Expression Analysis](https://mlsanity.com/paper/2203.12367/) (2022)
- [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)
- [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)
- [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025)
- [Multi-modal Facial Affective Analysis based on Masked Autoencoder](https://mlsanity.com/paper/2303.10849/) (2023)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3](https://mlsanity.com/paper/2203.13031/) (2022)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)
- [Leveraging TCN and Transformer for effective visual-audio fusion in continuous emotion recognition](https://mlsanity.com/paper/2303.08356/) (2023)
- [BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2310.03485/) (2023)
- [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)
- [Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices](https://mlsanity.com/paper/2203.13436/) (2022)
- [MMA-MRNNet: Harnessing Multiple Models of Affect and Dynamic Masked RNN for Precise Facial Expression Intensity Estimation](https://mlsanity.com/paper/2303.00180/) (2023)
- [Robust Light-Weight Facial Affective Behavior Recognition with CLIP](https://mlsanity.com/paper/2403.09915/) (2024)
- [Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit](https://mlsanity.com/paper/2409.17717/) (2024)
- [Multi-modal Emotion Estimation for in-the-wild Videos](https://mlsanity.com/paper/2203.13032/) (2022)
- [Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport](https://mlsanity.com/paper/2401.15489/) (2024)
- [Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2403.13659/) (2024)
- [HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition](https://mlsanity.com/paper/2503.10399/) (2025)
- [Joint Multimodal Transformer for Emotion Recognition in the Wild](https://mlsanity.com/paper/2403.10488/) (2024)
- [ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment](https://mlsanity.com/paper/2308.14448/) (2023)
- [DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios](https://mlsanity.com/paper/2506.05372/) (2025)

## Abstract (arXiv)

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) Multi-Task-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present the baseline systems along with their obtained results.