1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track
This addresses domain shift issues in object detection for computer vision applications, but it is incremental as it builds on existing domain generalization and adaptation techniques.
The paper tackled out-of-distribution generalization in object detection by proposing a Generalize-then-Adapt framework, achieving first place in the ECCV 2022 OOD-CV challenge.
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.