mlsanityData to 14 Oct 2026

Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks

arXiv 2207.10025 ↗First posted 20 Jul 2022cs.CVcited by 5

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Abstract (arXiv)

Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. In this paper, we propose a multi-task learning-based facial expression recognition approach which consists of emotion and appearance learning branches that can share all face information, and present preliminary results for the LSD challenge introduced in the 4th affective behavior analysis in-the-wild (ABAW) competition. Our method achieved the mean F1 score of 0.71.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

21 of 33 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  4. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  5. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 624
  6. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  7. 2020Learning an Animatable Detailed 3D Face Model from In-The-Wild Imagescited by 477
  8. 2018Deep Facial Expression Recognition: A Surveycited by 270
  9. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  10. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  11. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  12. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
9 more
  1. 2022Separable Self-attention for Mobile Vision Transformerscited by 163
  2. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  3. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  4. 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
  5. 2016From Facial Expression Recognition to Interpersonal Relation Predictioncited by 53
  6. 2018Photorealistic Facial Synthesis in the Dimensional Affect Spacecited by 33
  7. 2022MixAugment & Mixup: Augmentation Methods for Facial Expression Recognitioncited by 31
  8. 2015Learning Social Relation Traits from Face Imagescited by 25
  9. 2020Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognitioncited by 24

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3.

5 papers.

  1. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  2. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  3. 2024Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyondcited by 60
  4. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  5. 2022Affective Behaviour Analysis Using Pretrained Model with Facial Prioricited by 10