mlsanityData to 7 Oct 2026

Multi-Label Class Balancing Algorithm for Action Unit Detection

arXiv 2002.03238 ↗First posted 8 Feb 2020cs.CV cs.LG eess.IVcited by 7

Not on the map. Why?

Abstract (arXiv)

Isolated facial movements, so-called Action Units, can describe combined emotions or physical states such as pain. As datasets are limited and mostly imbalanced, we present an approach incorporating a multi-label class balancing algorithm. This submission is subject to the Action Unit detection task of the Affective Behavior Analysis in-the-wild (ABAW) challenge at the IEEE Conference on Face and Gesture Recognition 2020.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 2.

7 papers.

  1. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited 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. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  5. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  6. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  7. 2022An Attention-based Method for Action Unit Detection at the 3rd ABAW Competitioncited by 4