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In this paper, we present our solutions for the 5th Workshop and Competitionon Affective Behavior Analysis in-the-wild (ABAW), which includes foursub-challenges of Valence-Arousal (VA) Estimation, Expression (Expr)Classification, Action Unit (AU) Detection and Emotional Reaction Intensity(ERI) Estimation. The 5th ABAW competition focuses on facial affect recognitionutilizing different modalities and datasets. In our work, we extract powerfulaudio and visual features using a large number of sota models. These featuresare fused by Transformer Encoder and TEMMA. Besides, to avoid the possibleimpact of large dimensional differences between various features, we design anAffine Module to align different features to the same dimension. Extensiveexperiments demonstrate that the superiority of the proposed method. For the VAEstimation sub-challenge, our method obtains the mean Concordance CorrelationCoefficient (CCC) of 0.6066. For the Expression Classification sub-challenge,the average F1 Score is 0.4055. For the AU Detection sub-challenge, the averageF1 Score is 0.5296. For the Emotional Reaction Intensity Estimationsub-challenge, the average pearson's correlations coefficient on the validationset is 0.3968. All of the results of four sub-challenges outperform thebaseline with a large margin.

Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challenge