  OOD-CV challenge is an out-of-distribution generalization task. To solve this
problem in object detection track, we propose a simple yet effective
Generalize-then-Adapt (G&A) framework, which is composed of a two-stage domain
generalization part and a one-stage domain adaptation part. The domain
generalization part is implemented by a Supervised Model Pretraining stage
using source data for model warm-up and a Weakly Semi-Supervised Model
Pretraining stage using both source data with box-level label and auxiliary
data (ImageNet-1K) with image-level label for performance boosting. The domain
adaptation part is implemented as a Source-Free Domain Adaptation paradigm,
which only uses the pre-trained model and the unlabeled target data to further
optimize in a self-supervised training manner. The proposed G&A framework help
us achieve the first place on the object detection leaderboard of the OOD-CV
challenge. Code will be released in
https://github.com/hikvision-research/OOD-CV.
