In this technical report, we introduce TempT, a novel method for test time
adaptation on videos by ensuring temporal coherence of predictions across
sequential frames. TempT is a powerful tool with broad applications in computer
vision tasks, including facial expression recognition (FER) in videos. We
evaluate TempT's performance on the AffWild2 dataset as part of the Expression
Classification Challenge at the 5th Workshop and Competition on Affective
Behavior Analysis in the wild (ABAW). Our approach focuses solely on the
unimodal visual aspect of the data and utilizes a popular 2D CNN backbone, in
contrast to larger sequential or attention based models. Our experimental
results demonstrate that TempT has competitive performance in comparison to
previous years reported performances, and its efficacy provides a compelling
proof of concept for its use in various real world applications.

TempT 是一种新的测试时间适应方法，通过确保连续帧之间的预测的时间一致性，可以在视频中进行面部表情识别等计算机视觉任务中进行广泛应用的强大工具。我们使用流行的 2D CNN 基础架构对 TempT 在 AffWild2 数据集上进行性能评估，并证明其在与先前报告的性能相比具有竞争力，其有效性为其在各种现实世界应用中的使用提供了令人信服的概念证明。