ABAW : Facial Expression Recognition in the wild
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Abstract (arXiv)
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification challenge using multiple approaches such as fully supervised, semi-supervised and noisy label approach. Our approach using noise aware model has performed better than baseline model by 10.46% and semi supervised model has performed better than baseline model by 9.38% and the fully supervised model has performed better than the baseline by 9.34%
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 17 Mar 2023.
Builds on
23 of 31 references.
References · 23
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 621
- 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
- 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
- 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
- 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
- 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
- 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
- 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
- 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
- 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
- 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
11 more
- 2021Dash: Semi-Supervised Learning with Dynamic Thresholdingcited by 147
- 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
- 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
- 2018Aff-Wild2: Extending the Aff-Wild Database for Affect Recognitioncited by 80
- 2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
- 2018Photorealistic Facial Synthesis in the Dimensional Affect Spacecited by 33
- 2022Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margincited by 32
- 2018A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressionscited by 25
- 2022MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularizationcited by 22
- 2022Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challengescited by 10
- 2022Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognitioncited by 2
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Citing papers per year: 2024: 2; 2025: 0; 2026: 1.
3 papers.