论文标题
集群级伪标签，用于无源跨域面部表达识别
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
论文作者
论文摘要
从视觉数据中自动理解情绪是人类行为理解的基本任务。尽管设计用于面部表达识别的模型（FER）在许多数据集上表现出了出色的性能，但由于域移位，在在不同数据集中接受训练和测试时，它们通常会遭受严重的性能降解。此外，由于面部图像被认为是高度敏感的数据，因此通常会拒绝大规模数据集的可访问性。在这项工作中，我们通过提出第一个无源无监督的域适应性（SFUDA）方法来解决上述问题。我们的方法利用了自制的预处理来从目标数据中学习良好的特征表示形式，并提出了一种新颖而强大的集群级伪标记策略，以说明集群内统计。我们在四个适应设置中验证方法的有效性，证明当应用于FER时，它始终优于现有的SFUDA方法，并且与在UDA设置中解决FER的方法相当。
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have demonstrated excellent performances on many datasets, they often suffer from severe performance degradation when trained and tested on different datasets due to domain shift. In addition, as face images are considered highly sensitive data, the accessibility to large-scale datasets for model training is often denied. In this work, we tackle the above-mentioned problems by proposing the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for FER. Our method exploits self-supervised pretraining to learn good feature representations from the target data and proposes a novel and robust cluster-level pseudo-labelling strategy that accounts for in-cluster statistics. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER, and is on par with methods addressing FER in the UDA setting.
