论文标题
从面部表情中对狗情绪状态进行自动分类的深度学习模型
Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions
论文作者
论文摘要
与人类类似，动物的面部表情与情绪状态紧密相关。但是，与人类领域相反，动物面部表情对情绪状态的自动识别并没有充满反感，这主要是由于数据收集和建立地面真相的困难，涉及非语言使用者的情绪状态。我们将最近的深度学习技术应用于在受控的实验环境中收集的数据集上对狗的挫败进行分类和（负面）的挫败感。我们探讨了在此任务的不同监督下不同骨干（例如重新连接，VIT）的适用性，并发现自我监督的预定的VIT（DINO-VIT）的特征优于其他替代方案。据我们所知，这项工作是第一个解决对受控实验中获得的数据自动分类的任务。
Similarly to humans, facial expressions in animals are closely linked with emotional states. However, in contrast to the human domain, automated recognition of emotional states from facial expressions in animals is underexplored, mainly due to difficulties in data collection and establishment of ground truth concerning emotional states of non-verbal users. We apply recent deep learning techniques to classify (positive) anticipation and (negative) frustration of dogs on a dataset collected in a controlled experimental setting. We explore the suitability of different backbones (e.g. ResNet, ViT) under different supervisions to this task, and find that features of a self-supervised pretrained ViT (DINO-ViT) are superior to the other alternatives. To the best of our knowledge, this work is the first to address the task of automatic classification of canine emotions on data acquired in a controlled experiment.
