  OOD-CV challenge is an out-of-distribution generalization task. In this
challenge, our core solution can be summarized as that Noisy Label Learning Is
A Strong Test-Time Domain Adaptation Optimizer. Briefly speaking, our main
pipeline can be divided into two stages, a pre-training stage for domain
generalization and a test-time training stage for domain adaptation. We only
exploit labeled source data in the pre-training stage and only exploit
unlabeled target data in the test-time training stage. In the pre-training
stage, we propose a simple yet effective Mask-Level Copy-Paste data
augmentation strategy to enhance out-of-distribution generalization ability so
as to resist shape, pose, context, texture, occlusion, and weather domain
shifts in this challenge. In the test-time training stage, we use the
pre-trained model to assign noisy label for the unlabeled target data, and
propose a Label-Periodically-Updated DivideMix method for noisy label learning.
After integrating Test-Time Augmentation and Model Ensemble strategies, our
solution ranks the first place on the Image Classification Leaderboard of the
OOD-CV Challenge. Code will be released in
https://github.com/hikvision-research/OOD-CV.
