Abstract
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge. In this paper we have dealt only expression classification challenge using multiple approaches such as fully supervised, semi-supervised and noisy label approach. Our approach using noise aware model has performed better than baseline model by 10.46% and semi supervised model has performed better than baseline model by 9.38% and the fully supervised model has performed better than the baseline by 9.34%
Abstract (translated)
这篇文献讨论了第五项野生情感行为分析挑战(ABAW)竞赛,其中包括多个挑战,例如情感强度估计挑战、表达分类挑战、动作单元检测挑战和情绪反应强度估计挑战。在本文中,我们使用多个方法,如完全监督、半监督和噪声标签方法,来处理表达分类挑战。我们使用噪声意识到模型的方法表现得更好,比基准模型提高了10.46%。半监督模型表现得更好,比基准模型提高了9.38%。完全监督模型表现得更好,比基准提高了9.34%。
URL
https://arxiv.org/abs/2303.09785
