CVPR 2023 – Robust Test-Time Adaptation in Dynamic Scenarios

In this episode we discuss Robust Test-Time Adaptation in Dynamic Scenarios
by Longhui Yuan, Binhui Xie, Shuang Li. The paper discusses the limitations of test-time adaptation (TTA) methods in dynamic scenarios where the test data is sampled gradually over time, and proposes a new method called Robust Test-Time Adaptation (RoTTA) to address these limitations. RoTTA includes a robust batch normalization scheme, a memory bank for category-balanced data sampling, and a time-aware reweighting strategy with a teacher-student model to stabilize the training procedure. The paper presents extensive experiments to prove the effectiveness of RoTTA in continual test-time adaptation on correlatively sampled data streams, making it an easy-to-implement choice for rapid deployment.