论文标题
HSE-NN团队在第四届ABAW比赛中：多任务情感识别和从合成图像中学习
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images
论文作者
论文摘要
在本文中，我们介绍了HSE-NN团队在第四次竞争中有关情感行为分析（ABAW）的结果。新型的多任务效率网络模型经过训练，以同时识别面部表情以及对静态照片的价和唤醒的预测。由此产生的MT-Emotieffnet提取了在多任务学习挑战中馈入简单的前馈神经网络中的视觉特征。我们在验证集上获得了性能度量1.3，与基线（0.3）的性能或仅在S-Aff-Wild2数据库中训练的现有模型相比，这要大大更大。在从合成数据挑战中学习的过程中，使用超分辨率技术（例如Real-Esrgan）提高了原始合成训练集的质量。接下来，在新的培训套件中对MT-Emotieffnet进行了微调。最终预测是预先训练和微调的MT-Emotieffnets的简单混合集合。我们的平均验证F1得分比基线卷积神经网络高18％。
In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior Analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline convolutional neural network.
