This article presents our results for the sixth Affective Behavior Analysis
in-the-wild (ABAW) competition. To improve the trustworthiness of facial
analysis, we study the possibility of using pre-trained deep models that
extract reliable emotional features without the need to fine-tune the neural
networks for a downstream task. In particular, we introduce several lightweight
models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN
architectures trained in multi-task scenarios to recognize facial expressions,
valence, and arousal on static photos. These neural networks extract
frame-level features fed into a simple classifier, e.g., linear feed-forward
neural network, to predict emotion intensity, compound expressions, action
units, facial expressions, and valence/arousal. Experimental results for five
tasks from the sixth ABAW challenge demonstrate that our approach lets us
significantly improve quality metrics on validation sets compared to existing
non-ensemble techniques.

本论文介绍了利用预训练的深度模型在静态照片上提取可靠情感特征的可能性，并通过多任务场景中的轻量级模型来识别面部表情、价值和唤起。实验结果表明，与现有的非集成技术相比，我们的方法显著提高了验证集上的质量指标。