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In this paper, we present the results of the HSE-NN team in the 4thcompetition on Affective Behavior Analysis in-the-wild (ABAW). The novelmulti-task EfficientNet model is trained for simultaneous recognition of facialexpressions and prediction of valence and arousal on static photos. Theresulting MT-EmotiEffNet extracts visual features that are fed into simplefeed-forward neural networks in the multi-task learning challenge. We obtainperformance measure 1.3 on the validation set, which is significantly greaterwhen compared to either performance of baseline (0.3) or existing models thatare trained only on the s-Aff-Wild2 database. In the learning from syntheticdata challenge, the quality of the original synthetic training set is increasedby using the super-resolution techniques, such as Real-ESRGAN. Next, theMT-EmotiEffNet is fine-tuned on the new training set. The final prediction is asimple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Ouraverage validation F1 score is 18% greater than the baseline convolutionalneural network.

HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images