arXiv:2203.13046
Facial Action Unit Recognition With Multi-models Ensembling
원문 초록
The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretrained by our private au and expression dataset, and Glint360K respectively. Finally, we ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU validation set.
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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample
Norface: Improving Facial Expression Analysis by Identity Normalization
Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
토론
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