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EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition
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In this article, the results of our team for the fifth Affective Behavior Analysis in-the-wild (ABAW) competition are presented. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model achieves a much greater macro-averaged F1-score for facial expression recognition and action unit detection and concordance correlation coefficients for valence/arousal estimation when compared to baseline.
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Cited by 1 Pith paper
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Task-Specific Feature Fusion Method for Multi-Task Affective Behavior Analysis
A task-adaptive system that mixes two frozen visual features with per-task fusion and temporal strategies scores 1.6341 on the ABAW11 validation set, beating its own shared multi-task baselines.
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