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.
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“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
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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
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Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets
Kopalidis, T., Solachidis, V., Vretos, N., & Daras, P. (2024) · Information · doi:10.3390/info15030135 · Cited by 205
A Review of Key Technologies for Emotion Analysis Using Multimodal Information
Zhu, X., Guo, C., Feng, H., Huang, Y., Feng, Y., Wang, X., & Wang, R. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10287-z · Cited by 143
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition
Praveen, R. G., de Melo, W. C., Ullah, N., Aslam, M. H., Zeeshan, O., Denorme, T., Pedersoli, M., Koerich, A. L., Bacon, S., Cardinal, P., et al. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00278 · Cited by 115
Transformer-based Multimodal Information Fusion for Facial Expression Analysis
Zhang, W., Qiu, F., Wang, S., Zeng, H., Zhang, Z., An, R., Ma, B., & Ding, Y. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00271 · Cited by 114
Kollias, D., Tzirakis, P., Baird, A., Cowen, A., & Zafeiriou, S. P. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00626 · Cited by 102
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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
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