mlsanityData to 7 Oct 2026

Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator

arXiv 2203.13072 ↗First posted 24 Mar 2022cs.CV eess.IVcited by 4

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Abstract (arXiv)

Due to the collection of big data and the development of deep learning, research to predict human emotions in the wild is being actively conducted. We designed a multi-task model using ABAW dataset to predict valence-arousal, expression, and action unit through audio data and face images at in real world. We trained model from the incomplete label by applying the knowledge distillation technique. The teacher model was trained as a supervised learning method, and the student model was trained by using the output of the teacher model as a soft label. As a result we achieved 2.40 in Multi Task Learning task validation dataset.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 24 Mar 2022.

Builds on

20 of 28 references.

References · 20

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  3. 2019Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillationcited by 491
  4. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  5. 2016SoundNet: Learning Sound Representations from Unlabeled Videocited by 244
  6. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  7. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  8. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  9. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  10. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  11. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  12. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
8 more
  1. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  2. 2020Two-Stream Aural-Visual Affect Analysis in the Wildcited by 51
  3. 2020Multitask Emotion Recognition with Incomplete Labelscited by 39
  4. 2021Iterative Distillation for Better Uncertainty Estimates in Multitask Emotion Recognitioncited by 20
  5. 2021Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competitioncited by 18
  6. 2021Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusioncited by 15
  7. 2020A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wildcited by 8
  8. 2021Multi-modal Affect Analysis using standardized data within subjects in the Wildcited by 4

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  2. 2022Affective Behaviour Analysis Using Pretrained Model with Facial Prioricited by 10
  3. 2025Towards Unified Music Emotion Recognition across Dimensional and Categorical Modelscited by 7
  4. 2022BYEL : Bootstrap Your Emotion Latentcited by 3