mlsanityData to 7 Oct 2026

Multi-label Transformer for Action Unit Detection

arXiv 2203.12531 ↗First posted 23 Mar 2022cs.CVcited by 9

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Abstract (arXiv)

Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face representations and has therefore aroused great interest in the past few years. One of the main obstacles toward building efficient deep learning based AU detection system is the lack of wide facial image databases annotated by AU experts. In that extent the ABAW challenge paves the way toward better AU detection as it involves a 2M frames AU annotated dataset. In this paper, we present our submission to the ABAW3 challenge. In a nutshell, we applied a multi-label detection transformer that leverage multi-head attention to learn which part of the face image is the most relevant to predict each AU.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

20 of 30 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  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. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 126
  3. 2020JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attentioncited by 62
  4. 2018Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignmentcited by 55
  5. 2018Facial Action Unit Detection Using Attention and Relation Learningcited by 46
  6. 2018Deep Structure Inference Network for Facial Action Unit Recognitioncited by 38
  7. 2017EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detectioncited by 26
  8. 2022Multi-Order Networks for Action Unit Detectioncited by 6

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 5.

9 papers.

  1. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  2. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  3. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  4. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  5. 2024Affective Behaviour Analysis via Integrating Multi-Modal Knowledgecited by 18
  6. 2024Affective Behaviour Analysis via Progressive Learningcited by 7
  7. 2023Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challengecited by 6
  8. 2024Facial Affect Recognition based on Multi Architecture Encoder and Feature Fusion for the ABAW7 Challengecited by 2
  9. 2022Multi-Task Transformer with uncertainty modelling for Face Based Affective Computingcited by 0