1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track

• URL: http://arxiv.org/abs/2301.04796v1
• Date: Thu, 12 Jan 2023 03:50:46 GMT
• Title: 1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track
• Authors: Wei Zhao, Binbin Chen, Weijie Chen, Shicai Yang, Di Xie, Shiliang Pu, Yueting Zhuang
• Abstract summary: Generalize-then-Adapt (G&A) framework is composed of a two-stage domain generalization part and a one-stage domain adaptation part. The proposed G&A framework help us achieve the first place on the object detection leaderboard of the OOD-CV challenge.
• Score: 71.12470906323298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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