# Analysing Affective Behavior in the second ABAW2 Competition

arXiv: https://arxiv.org/abs/2106.15318 · HTML: https://mlsanity.com/paper/2106.15318/

## Key facts

- Cited by 163 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 90 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [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)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (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)
- [Two-Stream Aural-Visual Affect Analysis in the Wild](https://mlsanity.com/paper/2002.03399/) (2020)
- [SSH: Single Stage Headless Face Detector](https://mlsanity.com/paper/1708.03979/) (2017)
- [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)
- [FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition](https://mlsanity.com/paper/1609.06591/) (2016)
- [The OMG-Emotion Behavior Dataset](https://mlsanity.com/paper/1803.05434/) (2018)
- [A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions](https://mlsanity.com/paper/1811.07771/) (2018)
- [Peak-Piloted Deep Network for Facial Expression Recognition](https://mlsanity.com/paper/1607.06997/) (2016)
- [Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm](https://mlsanity.com/paper/1809.04359/) (2018)
- [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)
- [Efficient Augmentation via Data Subsampling](https://mlsanity.com/paper/1810.05222/) (2018)
- [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)
- [An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild](https://mlsanity.com/paper/2010.03692/) (2020)

## Built on by (most cited first, 30 of 163)

- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges](https://mlsanity.com/paper/2202.10659/) (2022)
- [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)
- [Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition](https://mlsanity.com/paper/2212.05463/) (2022)
- [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)
- [Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos](https://mlsanity.com/paper/2112.00585/) (2021)
- [A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition](https://mlsanity.com/paper/2107.04187/) (2021)
- [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)
- [COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition](https://mlsanity.com/paper/2206.05833/) (2022)
- [Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognition](https://mlsanity.com/paper/2108.04228/) (2021)
- [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)
- [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)
- [Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention](https://mlsanity.com/paper/2209.09068/) (2022)
- [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)

## Abstract (arXiv)

The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect. ABAW2 is split into three Challenges, each one addressing one of the three main behavior tasks of valence-arousal estimation, basic expression classification and action unit detection. All three 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 for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with ICCV 2021. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics and present the baseline system with its results. More information regarding the Competition is provided in the Competition site: https://ibug.doc.ic.ac.uk/resources/iccv-2021-2nd-abaw.