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
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
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
Self-citationABAW: Learning from Synthetic Data & Multi-task Learning Challenges
Kollias, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_12 · Cited by 80
Self-citationThe 6th Affective Behavior Analysis in-the-wild (ABAW) Competition
Kollias, D., Tzirakis, P., Cowen, A., Zafeiriou, S. P., Kotsia, I., Baird, A., Gagne, C., Shao, C., & Hu, G. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00461 · Cited by 59
Self-citationZheng, W., Yu, J., Xia, R., & Wang, S. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.861 · Cited by 51
Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition
Praveen, R. G., & Alam, M. J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00483 · Cited by 54
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition
Xue, F., Tan, Z., Zhu, Y. M., Ma, Z., & Guo, G. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00269 · Cited by 46
MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition
Psaroudakis, A., & Kollias, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00264 · Cited by 46
Self-citationUncertain Facial Expression Recognition via Multi-Task Assisted Correction
Liu, Y. M., Zhang, X., Kauttonen, J., & Zhao, G. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3301209 · Cited by 45
Video-based Frame-level Facial Analysis of Affective Behavior on Mobile Devices using EfficientNets
Savchenko, A. V. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00263 · Cited by 50
AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit
Bishay, M., Preston, K., Strafuss, M., Page, G., Turcot, J., & Mavadati, M. (2023) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg57933.2023.10042673 · Cited by 40
Exploring facial expressions and action unit domains for Parkinson detection
Gomez, L. F., Morales, A., Fiérrez, J., & Orozco‐Arroyave, J. R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281248 · Cited by 39
Beyond Supervised Learning for Pervasive Healthcare
Gu, X., Deligianni, F., Han, J., Liu, X., Chen, W., Yang, G., & Lo, B. P. L. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3296938 · Cited by 44
Leveraging TCN and Transformer for effective visual-audio fusion in continuous emotion recognition
Zhou, W., Lu, J., Xiong, Z., & Wang, W. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00610 · Cited by 37
Frame Level Emotion Guided Dynamic Facial Expression Recognition with Emotion Grouping
Lee, B., Shin, H., Ku, B., & Ko, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00602 · Cited by 39
Joint Multimodal Transformer for Emotion Recognition in the Wild
Waligora, P., Aslam, M. H., Zeeshan, M., Belharbi, S., Koerich, A. L., Pedersoli, M., Bacon, S., & Granger, É. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00465 · Cited by 34
7th ABAW Competition: Multi-task Learning and Compound Expression Recognition
Kollias, D., Zafeiriou, S. P., Kotsia, I., Dhall, A., Ghosh, S., Shao, C., & Hu, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_3 · Cited by 34
Self-citationGraph-Based Facial Affect Analysis: A Review
Liu, Y. M., Zhang, X., Li, Y., Zhou, J., Li, X., & Zhao, G. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3215918 · Cited by 42
Multi-modal Facial Affective Analysis based on Masked Autoencoder
Zhang, W., Ma, B., Qiu, F., & Ding, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00615 · Cited by 34
Moorthy, S., & Moon, Y.-K. (2025) · Mathematics · doi:10.3390/math13071100 · Cited by 31
Zhang, S., An, R., Ding, Y., & Guan, C. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00265 · Cited by 30
Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5
Zhang, S., Zhao, Z., & Guan, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00611 · Cited by 27
Exploring Facial Expression Recognition through Semi-Supervised Pre-training and Temporal Modeling
Yu, J., Wei, Z., Cai, Z., Zhao, G., Zhang, Z., Wang, Y., Xie, G., Zhu, J., Zhu, W., Liu, Q., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00492 · Cited by 27
Time-Continuous Audiovisual Fusion with Recurrence vs Attention for In-The-Wild Affect Recognition
Karas, V., Tellamekala, M. K., Mallol-Ragolta, A., Valstar, M., & Schuller, B. W. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00266 · Cited by 27
Privileged Knowledge Distillation for Dimensional Emotion Recognition in the Wild
Aslam, M. H., Zeeshan, M., Pedersoli, M., Koerich, A. L., Bacon, S., & Granger, É. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00336 · Cited by 25
Valence and Arousal Estimation based on Multimodal Temporal-Aware Features for Videos in the Wild
Meng, L., Liu, Y., Liu, X., Huang, Z., Jiang, W., Zhang, T., Liu, C., & Jin, Q. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00261 · Cited by 24
HSEmotion: High-speed emotion recognition library
Savchenko, A. (2022) · Software Impacts · doi:10.1016/j.simpa.2022.100433 · Cited by 24
MT-EmotiEffNet for Multi-task Human Affective Behavior Analysis and Learning from Synthetic Data
Savchenko, A. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_4 · Cited by 24
An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition
Aguilera, A., Mellado, D., & Rojas, F. (2023) · Sensors · doi:10.3390/s23115184 · Cited by 24
Savchenko, A. V. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00606 · Cited by 24
Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition
Yu, J., Cai, Z., Li, R., Zhao, G., Xie, G., Zhu, J., Zhu, W., Ling, Q., Wang, L., Wang, C., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00616 · Cited by 24
Zhang, Z., An, L., Cui, Z., Xu, A., Dong, T., Jiang, Y., Shi, J., Liu, X., Sun, X., & Wang, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00607 · Cited by 23
Li, J., Chen, Y., Zhang, X., Nie, J., Li, Z., Yu, Y., Zhang, Y., Hong, R., & Wang, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00620 · Cited by 22
CAGE: Circumplex Affect Guided Expression Inference
Wagner, N., Mätzler, F., Vossberg, S. R., Schneider, H., Pavlitska, S., & Zöllner, J. M. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00471 · Cited by 25
Incongruity-Aware Cross-Modal Attention for Audio-Visual Fusion in Dimensional Emotion Recognition
Praveen, R. G., & Alam, J. (2024) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2024.3422823 · Cited by 21
Multi-Task Learning Framework for Emotion Recognition In-the-Wild
Zhang, T., Liu, C., Liu, X., Liu, Y., Meng, L., Sun, L., Jiang, W., Zhang, F., Zhao, J., & Jin, Q. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_11 · Cited by 20
An Ensemble Approach for Facial Behavior Analysis in-the-wild Video
Nguyen, H.-H., Huynh, V. T., & Kim, S.-H. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00281 · Cited by 19
A Unified Approach to Facial Affect Analysis: the MAE-Face Visual Representation
Ma, B., Zhang, W., Qiu, F., & Ding, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00630 · Cited by 17
Robust Light-Weight Facial Affective Behavior Recognition with CLIP
Lin, L., Papabathini, S., Wang, X., & Hu, S. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00103 · Cited by 16
EmoStyle: One-Shot Facial Expression Editing Using Continuous Emotion Parameters
Azari, B., & Lim, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00626 · Cited by 15
Zero-Shot Audio-Visual Compound Expression Recognition Method based on Emotion Probability Fusion
Рюмина, Е. В., Markitantov, M., Ryumin, D., Kaya, H., & Karpov, A. A. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00478 · Cited by 16
Jeong, J.-Y., Hong, Y.-G., Kim, D., Jeong, J.-W., Jung, Y., & Kim, S.-H. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00262 · Cited by 14
Affective Behaviour Analysis Using Pretrained Model with Facial Prior
Li, Y., Sun, H., Liu, Z., Han, H., & Shan, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_2 · Cited by 13
An Effective Ensemble Learning Framework for Affective Behaviour Analysis
Zhang, W., Qiu, F., 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/cvprw63382.2024.00479 · Cited by 13
Zhou, L., Feng, Z., Wang, H., & Guo, Q. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2247606 · Cited by 12
Mehta, A., & Yang, W. (2023) · International Conference on Video and Image Processing (ICVIP) · doi:10.1145/3639390.3639392 · Cited by 15
Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition
Kollias, D., Tzirakis, P., Cowen, A., Zafeiriou, S. P., Kotsia, I., Granger, É., Pedersoli, M., Bacon, S., Baird, A., Gagne, C., et al. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00554 · Cited by 12
Self-citationYu, J., Li, R., Cai, Z., Zhao, G., Xie, G., Zhu, J., Zhu, W., Ling, Q., Wang, L., Wang, C., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00614 · Cited by 12
GameVibe: a multimodal affective game corpus
Barthet, M., Kaselimi, M., Pinitas, K., Makantasis, K., Liapis, A., & Yannakakis, G. N. (2024) · Scientific Data · doi:10.1038/s41597-024-04022-4 · Cited by 12
Model Level Ensemble for Facial Action Unit Recognition at the 3rd ABAW Challenge
Jiang, W., Wu, Y. N., Qiao, F., Meng, L., Deng, Y., & Liu, C. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00260 · Cited by 11
Spatial-Temporal Graph-Based AU Relationship Learning for Facial Action Unit Detection
Wang, Z., Song, S., Luo, C., Zhou, Y., Wu, S., Xie, W., & Shen, L. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00627 · Cited by 19
Kollias, D., Psaroudakis, A., Αρσένος, Α., Theofilou, P., Shao, C., Hu, G., & Patras, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_1 · Cited by 11
Self-citationFacial Expression Classification using Fusion of Deep Neural Network in Video
Phan, K. N., Nguyen, H.-H., Huynh, V. T., & Kim, S.-H. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00280 · Cited by 10
Savchenko, A. V., & Sidorova, A. P. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00488 · Cited by 10
Cabacas-Maso, J., Ortega-Beltrán, E., Benito-Altamirano, I., & Ventura, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_22 · Cited by 10
Mamba-VA: A Mamba-Based Approach for Continuous Emotion Recognition in Valence-Arousal Space
Liang, Y., Wang, Z., Liu, F., Liu, M., & Yao, Y. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00562 · Cited by 14
Empirical Validation of an Agent-Based Model of Emotion Contagion
van Haeringen, E. V., Veltmeijer, E. A., & Gerritsen, C. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3272031 · Cited by 9
Moreno D’Incà, Beyan, C., Niewiadomski, R., Barattin, S., & Sebe, N. (2023) · IEEE Access · doi:10.1109/access.2023.3308047 · Cited by 11
Action unit detection by exploiting spatial-temporal and label-wise attention with transformer
Wang, L., Qi, J., Cheng, J., & Suzuki, K. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00276 · Cited by 10
Savchenko, A. V. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00473 · Cited by 8
Xu, C., Du, Y., Wang, J., Zheng, W., Li, T., & Yuan, Z.-S. (2023) · Computational Intelligence · doi:10.1111/coin.12607 · Cited by 9
Emotion Recognition beyond Pixels: Leveraging Facial Point Landmark Meshes
Arabian, H., Alshirbaji, T. A., Chase, J. G., & Möeller, K. (2024) · Applied Sciences · doi:10.3390/app14083358 · Cited by 9
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features
Witherow, M. A., Samad, M. D., Diawara, N., Bar, H., & Iftekharuddin, K. M. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3297075 · Cited by 9
BFCP: Pursue Better Forward Compatibility Pretraining for Few-Shot Class-Incremental Learning
Fu, Z., Wang, Z., Xu, X., Guo, W., Chi, Z., Yang, H., & Du, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3548465 · Cited by 8
Multimodal emotion recognition method in complex dynamic scenes
Liu, L., Luo, Q., Zhang, W., Zhang, M., & Zhai, B. (2025) · Journal of Information and Intelligence · doi:10.1016/j.jiixd.2025.02.004 · Cited by 12
Emotion Recognition Using Transformers with Random Masking
Min, S., Yang, J.-S., & Lim, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00489 · Cited by 8
Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression Recognition
Nguyen, X.-B., Nguyen, H.-T., Nguyen, T.-H., Do, N.-T., & Dinh, V. Q. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00481 · Cited by 8
Yu, J., Zhu, W., Zhu, J., Cai, Z., Zhao, G., Zhang, Z., Xie, G., Wei, Z., Liu, Q., & Liang, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00490 · Cited by 8
Nonverbal Communication Cue Recognition: A Pathway to More Accessible Communication
Shafique, Z., Wang, H., & Tian, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00600 · Cited by 7
A Dual Branch Network for Emotional Reaction Intensity Estimation
Yu, J., Zhu, J., Zhu, W., Cai, Z., Xie, G., Li, R., Zhao, G., Ling, Q., Wang, L., Wang, C., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00617 · Cited by 7
Graph-Based Multi-Feature Fusion Method for Speech Emotion Recognition
Liu, X., Jie, L., & Wang, C. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142450023x · Cited by 7
Kılıç, A. C., Karakuş, A., & Alptekin, S. E. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.323 · Cited by 7
Lei, J., Liu, Z., Zou, Z., Li, T., Xu, J., Wang, S., Yang, G., & Feng, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_7 · Cited by 7
Xue, F., Sun, Y., & Yang, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00604 · Cited by 7
Jeong, D. C., Shen, T., Liu, H., Kapoor, R., Nguyen, C., Liu, S., & Kitts, C. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342417 · Cited by 6
DEITalk: Speech-Driven 3D Facial Animation with Dynamic Emotional Intensity Modeling
Shen, K., Xia, H., Geng, G., Geng, G., Xia, S., & Ding, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681359 · Cited by 6
An Attention-based Method for Multi-label Facial Action Unit Detection
Hoai, D. L., Lim, E., Choi, E., Kim, S., Pant, S., Lee, G.-S., Kim, S.-H., & Yang, H.-J. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00274 · Cited by 6
Affective Behavior Analysis Using Action Unit Relation Graph and Multi-task Cross Attention
Nguyen, D.-K., Pant, S., Ho, N.-H., Lee, G.-S., Kim, S.-H., & Yang, H.-J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_10 · Cited by 6
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
Aslam, M. H., Zeeshan, M., Belharbi, S., Pedersoli, M., Koerich, A. L., Bacon, S., & Granger, É. (2024) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg59268.2024.10582047 · Cited by 6
Yu, J., Zhang, Z., Wei, Z., Zhao, G., Cai, Z., Wang, Y., Xie, G., Zhu, J., Zhu, W., Liu, Q., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00484 · Cited by 6
Ensemble Spatial and Temporal Vision Transformer for Action Units Detection
Vu, N. T., Huynh, V. T., Nguyen, T.-N., & Kim, S.-H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00612 · Cited by 7
Savchenko, A. V., & Savchenko, L. V. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00577 · Cited by 6
Yu, J., Zheng, Y., Wang, L., Wang, Y., & Xu, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00569 · Cited by 5
SAAML: A Framework for Semi-supervised Affective Adaptation via Metric Learning
Tran, M., Kim, Y., Su, C.-C., Kuo, C.-H., & Soleymani, M. (2023) · ACM Multimedia · doi:10.1145/3581783.3612286 · Cited by 6
Unimodal Multi-Task Fusion for Emotional Mimicry Intensity Prediction
Hallmen, T., Deuser, F., Oswald, N., & André, E. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00468 · Cited by 5
Okayama, M., & Hasegawa, T. (2025) · IEEE Access · doi:10.1109/access.2025.3565537 · Cited by 5
Leveraging Eye Movement for Instructing Robust Video-Based Facial Expression Recognition
Liu, Y., Wei, L., Liu, K., Chen, Z., Chen, Z., Tang, C., Chen, J., & Shan, S. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3599859 · Cited by 4
Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment
Tarigopula, N., Tornay, S., Sincan, O. M., Bowden, R., & Magimai.-Doss, M. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3531781 · Cited by 5
SetPeER: Set-Based Personalized Emotion Recognition With Weak Supervision
Tran, M., Yin, Y., & Soleymani, M. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3568024 · Cited by 4
Estimating Multiple Emotion Descriptors by Separating Description and Inference
Deng, D., & Shi, B. E. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00267 · Cited by 4
Multi-modal Emotion Reaction Intensity Estimation with Temporal Augmentation
Qiu, F., Ma, B., Zhang, W., & Ding, Y. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00613 · Cited by 4
Textualized and Feature-Based Models for Compound Multimodal Emotion Recognition in the Wild
Richet, N., Belharbi, S., Aslam, M. H., Schadt, M. E., González-González, M., Cortal, G., Koerich, A. L., Pedersoli, M., Finkel, A., Bacon, S., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91581-9_5 · Cited by 16
Two-Aspect Information Interaction Model for ABAW4 Multi-task Challenge
Sun, H., Lian, Z., Liu, B., Tao, J., Sun, L., Cai, C., & He, Y. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25075-0_13 · Cited by 4
Speech-Visual Emotion Recognition via Modal Decomposition Learning
Bai, L., Chang, R., Chen, G., & Zhou, Y. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3324294 · Cited by 4
Multi-modal Arousal and Valence Estimation under Noisy Conditions
Dresvyanskiy, D., Markitantov, M., Yu, J., Kaya, H., & Karpov, A. A. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00480 · Cited by 4
Semantic Matters: Multimodal Features for Affective Analysis
Hallmen, T., Kampa, R.-N., Deuser, F., Oswald, N., & André, E. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00570 · Cited by 3
Parihar, N., Kanan, Chawla, R., & Fortino, G. (2026) · Discover Robotics · doi:10.1007/s44430-026-00028-2 · Cited by 3
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
