mlsanityData to 7 Oct 2026

Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique

arXiv 2107.04192 ↗First posted 9 Jul 2021cs.CVcited by 10

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Abstract (arXiv)

The task of predicting affective information in the wild such as seven basic emotions or action units from human faces has gradually become more interesting due to the accessibility and availability of massive annotated datasets. In this study, we propose a method that utilizes the association between seven basic emotions and twelve action units from the AffWild2 dataset. The method based on the architecture of ResNet50 involves the multi-task learning technique for the incomplete labels of the two tasks. By combining the knowledge for two correlated tasks, both performances are improved by a large margin compared to those with the model employing only one kind of label.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 10 of its 15 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

10 of 15 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  4. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  5. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  6. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  7. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  8. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  9. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  10. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155

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Citing papers per year: 2021: 1; 2022: 6; 2023: 2; 2024: 0; 2025: 1.

10 papers.

  1. 2022Transformer-based Multimodal Information Fusion for Facial Expression Analysiscited by 49
  2. 2022Vision Transformer with Attentive Pooling for Robust Facial Expression Recognitioncited by 35
  3. 2023Multi-modal Facial Affective Analysis based on Masked Autoencodercited by 23
  4. 2022Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devicescited by 16
  5. 2022Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategiescited by 10
  6. 2022Facial Expression Classification using Fusion of Deep Neural Network in Video for the 3rd ABAW3 Competitioncited by 5
  7. 2022An Attention-based Method for Action Unit Detection at the 3rd ABAW Competitioncited by 4
  8. 2023A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factorscited by 4
  9. 2025ARPGNet: Appearance- and Relation-aware Parallel Graph Attention Fusion Network for Facial Expression Recognitioncited by 1
  10. 2021Consensual Collaborative Training And Knowledge Distillation Based Facial Expression Recognition Under Noisy Annotationscited by 0