Action Units Recognition by Pairwise Deep Architecture
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Abstract (arXiv)
In this paper, we propose a new automatic Action Units (AUs) recognition method used in a competition, Affective Behavior Analysis in-the-wild (ABAW). Our method tackles a problem of AUs label inconsistency among subjects by using pairwise deep architecture. While the baseline score is 0.31, our method achieved 0.67 in validation dataset of the competition.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2020.
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Citing papers per year: 2021: 3; 2022: 2.
5 papers.
- 2021Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competitioncited by 18
- 2021Action Units Recognition Using Improved Pairwise Deep Architecturecited by 6
- 2021Multi-modal Affect Analysis using standardized data within subjects in the Wildcited by 4
- 2022An Attention-based Method for Action Unit Detection at the 3rd ABAW Competitioncited by 4
- 2022Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Modelscited by 0
- 2022Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models15 Nov 2022
- 2022An Attention-based Method for Action Unit Detection at the 3rd ABAW Competition23 Mar 2022
- 2021Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition8 Jul 2021
- 2021Action Units Recognition Using Improved Pairwise Deep Architecture7 Jul 2021
- 2021Multi-modal Affect Analysis using standardized data within subjects in the Wild7 Jul 2021