The DOI matches its Crossref record; 61 of the 72 references it cites were found in a registry or an index.
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges
Open accessAbstract
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) MultiTask-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 both the baseline systems and the top performing teams’ per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams. More information regarding the Competition and the leaderboard for each Challenge can be found in the competition’s website: http://ibug.doc.ic.ac.uk/resources/cvpr-2022-3rd-abaw.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“we use the Aff-Wild2 dataset (Kollias and Zafeiriou, 2019; Kollias, 2022), which contains 548 video clips collected from YouTube in realworld environments. Each clip has several frames of aligned faces and each frame is annotated with a facial expression. It has eight classes of emotions (six basic emotions, neutral , and other ).”
From A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023) · cites this paper as Kollias, 2022
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
Deng, J., Guo, J., Ververas, E., Kotsia, I., & Zafeiriou, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00525 · Cited by 1,681
The Expression Of The Emotions In Man And Animals
Darwin, C. D. (1998) · doi:10.1093/oso/9780195112719.001.0001 · Cited by 733
Evidence of convergent validity on the dimensions of affect
Russell, J. A. (1978) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.36.10.1152 · Cited by 495
Facial Action Coding System (FACS)
Ekman, P. (2002) · Medical Entomology and Zoology · Cited by 433
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Kollias, D., Tzirakis, P., Nicolaou, M. A., Papaioannou, A., Zhao, G., Schuller, B. W., Kotsia, I., & Zafeiriou, S. P. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01158-4 · Cited by 406
Self-citationAff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge
Zafeiriou, S. P., Kollias, D., Nicolaou, M. A., Papaioannou, A., Zhao, G., & Kotsia, I. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.248 · Cited by 297
Self-citationAnalysing Affective Behavior in the First ABAW 2020 Competition
Kollias, D., Schulc, A., Hajiyev, E., & Zafeiriou, S. P. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00126 · Cited by 238
Self-citationFace Behavior a la carte: Expressions, Affect and Action Units in a Single NetworkPreprint
Kollias, D., Sharmanska, V., & Zafeiriou, S. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11111 · Cited by 228
Self-citationExpression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
Kollias, D., & Zafeiriou, S. (2019) · arXiv · doi:10.48550/arxiv.1910.04855 · Cited by 210
Self-citation
Flagged first, then by citations
11 of the 72 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- whissel, 1989, The dictionary of affect in language, emotion: Theory, research and experience: vol. 4, the measurement of emotions
- frijda, 1986, The Emotions
- goodfellow, 2016, Deep Learning
- cowie, 2000, ’feel-trace’: An instrument for recording perceived emotion in real time, ISCA Tutorial and Research Workshop (ITRW) on Speech and Emotion
Cited by
Sorted by citations
Items 101–148 of 148
Multi-task Learning for Human Affect Prediction with Auditory–Visual Synchronized Representation
Jeong, E., Oh, G., & Lim, S. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00272 · Cited by 3
Multi Model Ensemble for Compound Expression Recognition
Yu, J. R., Zhu, J., Zhu, W., Cai, Z., Zhao, G., Wei, Z., Xie, G., Zhang, Z., Liu, Q., & Liang, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00491 · Cited by 3
Language-guided Multi-modal Emotional Mimicry Intensity Estimation
Qiu, F., Zhang, W., Liu, C., Li, L., Du, H., Guo, T., & Yu, X. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00477 · Cited by 3
Praveen, R. G., Alam, J., & Charton, É. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00573 · Cited by 3
Honarmand, M., Mutlu, O. C., Azizian, P., Surabhi, S., & Wall, D. P. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00579 · Cited by 3
Interactive Multimodal Framework with Temporal Modeling for Emotion Recognition
Yu, J., Wang, Y., Wang, L., Zheng, Y., & Xu, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00568 · Cited by 3
Cabacas-Maso, J., Ortega-Beltrán, E., Benito-Altamirano, I., & Ventura, C. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00564 · Cited by 3
Yu, J., Zhu, L., Chi, Y., Zhang, Y., Zheng, Y., Wang, Y., & Lu, X. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00572 · Cited by 3
Jeong, J.-Y., Hong, S., Hong, S., Oh, J., Jung, Y., Kim, S., & Jeong, J.-W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_5 · Cited by 3
Wang, Z., Zheng, J., & Liu, F. (2023) · IET Image Processing · doi:10.1049/ipr2.12994 · Cited by 3
Unlocking the Black Box: Concept-Based Modeling for Interpretable Affective Computing Applications
Li, X., & Mahmoud, M. (2024) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg59268.2024.10581918 · Cited by 3
Affective Behavior Analysis Using Task-Adaptive and AU-Assisted Graph
Li, X., Du, W., & Yang, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_28 · Cited by 3
Ig3D: Integrating 3D Face Representations in Facial Expression Inference
Dong, L., Wang, X., Setlur, S., Govindaraju, V., & Nwogu, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_29 · Cited by 2
Shi, M., & Zheng, W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3625814 · Cited by 2
Three Techniques for Enhancing Emotional Expression on Embodied Avatar Face in VR
Jeong, J., Lee, G., Yang, H.-J., Kim, M., Shin, J., Suh, G., Kang, S., & Kim, S. (2025) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar67309.2025.00016 · Cited by 2
Рюмина, Е. В., Markitantov, M., Axyonov, A., Ryumin, D., Dolgushin, M., & Karpov, A. A. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00013 · Cited by 2
EmotiCrafter: Text-to-Emotional-Image Generation Based on Valence-Arousal Model
Dang, S., He, Y., Ling, L., Qian, Z., Zhao, N., & Cao, N. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01412 · Cited by 20
Facial Affect Recognition Using Semi-supervised Learning with Adaptive Threshold
Gera, D., Kumar, B. V. R., Badveeti, N. S. K., & Balasubramanian, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_3 · Cited by 2
Improving Valence-Arousal Estimation with Spatiotemporal Relationship Learning and Multimodal Fusion
Yu, J., Zhao, G., Wang, Y., Wei, Z., Zhang, Z., Cai, Z., Xie, G., Zhu, J., Zhu, W., Yang, S., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00785 · Cited by 2
FreeAvatar: Robust 3D Facial Animation Transfer by Learning an Expression Foundation Model
Qiu, F., Zhang, W., Liu, C., An, R., Li, L., Ding, Y., Fan, C., Hu, Z., & Yu, X. (2024) · SIGGRAPH Asia Conference Papers · doi:10.1145/3680528.3687669 · Cited by 3
Affective Behaviour Analysis via Progressive Learning
Liu, C., Zhang, W., Qiu, F., Li, L., Wang, D., & Yu, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_26 · Cited by 2
MAVEN: Multi-Modal Attention for Valence-Arousal Emotion Network
Ahire, V., Shah, K., Khan, M. N., Pakhale, N., Sookha, L. R., Ganaie, M. A., & Dhall, A. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00578 · Cited by 2
Multi-modal Information Fusion for Action Unit Detection in the Wild
Deng, Y., Liu, X., Meng, L., Jiang, W., Dong, Y., & Liu, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00622 · Cited by 2
Parallel Solution of Nonlinear Projection Equations in a Multitask Learning Framework
Wu, D., & Lisser, A. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3350335 · Cited by 2
Enhancing Explainability in Affective Signal Processing with Interpretable Machine Learning
Li, X. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00016 · Cited by 1
V-NAW: Video-Based Noise-Aware Adaptive Weighting for Facial Expression Recognition
Lee, J., Lee, K., Park, H., Kim, I.-J., & Nam, G. P. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00561 · Cited by 1
Aligning Multimodal Data for Fine-Grained Video Understanding via Cross-Attentive Recurrent Fusion
Kim, N., & Kim, J.-H. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00018 · Cited by 1
From Emotions to Violence: Multimodal Fine-Grained Behavior Analysis at the 9th ABAW
Kollias, D., Zafeiriou, S. P., Kotsia, I., Slabaugh, G., Senadeera, D. C., Zheng, J., Yadav, K. K. K., Shao, C., & Hu, G. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00006 · Cited by 1
Self-citationFacial Expression Recognition In-the-Wild with Deep Pre-trained Models
Li, S., Xu, Y., Wu, H., Wu, D., Yin, Y., Cao, J., & Ding, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_14 · Cited by 1
Liu, X., Shen, K., Yao, J., Wang, B., Wang, Y., Guan, Y., Liu, X., Li, G., An, L., Cui, Z., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_19 · Cited by 1
Lu, X., Yu, J., Zhang, Y., Zhu, L., Zheng, Y., Wang, Y., & Ling, Q. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00576 · Cited by 1
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style
Chen, Z., & Moscholios, S. (2024) · arXiv · doi:10.48550/arxiv.2410.03848
Multimodal continuous emotion analysis
Zhang, S. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/166783
Emotional Intensity-Aware Learning for Facial Expression Manipulation
Kim, S., & Song, B. C. (2023) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · doi:10.1109/itc-cscc58803.2023.10212641
Design of Intelligent models for Multimodal Socio-Affective Computing
Luna-Jiménez, C. (2023) · doi:10.20868/upm.thesis.74338
Kim, S., & Song, B. C. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3493416
Liang, Y., Hao, Y., Yao, Z., Liao, J., & Pan, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887765
Are We Friends? End-to-End Prediction of Child Rapport in Guided Play
Fraile, M., Varni, G., Lindblad, J., Sladoje, N., & Castellano, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_27
Multi-task Affective Behaviour Analysis Based on MT-EmotiNet Models
Savchenko, A. V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_17
Feng, S., Ma, J., & de Sa, V. R. (2025) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg61629.2025.11099285
Yu, J., Zhang, Y., Sun, F., Wang, L., Lu, R., Zhu, L., Lu, X., Zheng, Y., & Wang, Y. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00571
Lu, X., Wang, Z., Fu, Z., Xu, X., Zhang, Q., Xiao, T., & Du, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3627931
Emotion estimation from video footage with LSTM
Attrah, S. (2026) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1678984 · Cited by 1
Dynamic Temporal Gating Networks for Cross-Modal Valence-Arousal Estimation
Lee, Y., Lee, S., Park, C., Cha, J., & Park, E. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00012
Paincontrol: identity-preserving pain expression transfer with generative diffusion models
Zarghami, Y., Muzammil, M., Adeli, V., Reimer, H., Hadjistavropoulos, T., & Taati, B. (2026) · BioMedical Engineering OnLine · doi:10.1186/s12938-026-01561-2
From Cognitive Priors to Instance Semantics: A Unified Framework for Multi-task Affective Computing
Hu, G., Kollias, D., & Yang, X. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00825
Self-citationBeyond Emotion Labels: Multimodal AI Approaches to Affective Computing and Sentiment ModelingPreprint
Armendáriz-Íñigo, J. E., Meza-Tudela, A., Ji, Z., & Vidaurreta-Apesteguia, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6890916
Emotion-aware multimodal lightweight framework for adaptive voice interaction on edge device
Khuat, V.-D., Kim, J., Kim, S., Cao, Y., Maier, M., & Lim, W. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116458
Metadata
- Authors
- Kollias, D.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Kollias, Dimitrios
- ORCID
- Kollias, Dimitrios
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 2327-2335
- OpenAlex
- W4292794012
- Cited by
- 147
- References
- 72
- Citations by year
- 2026: 6 · 2025: 41 · 2024: 33 · 2023: 47 · 2022: 20
- FWCI
- 46.11
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- valence-arousal estimation, facial expression recognition, action unit detection, multi-task learning, Aff-Wild2, ABAW challenge, benchmark database
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review