1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track

• URL: http://arxiv.org/abs/2301.04795v1
• Date: Thu, 12 Jan 2023 03:44:30 GMT
• Title: 1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track
• Authors: Yilu Guo, Xingyue Shi, Weijie Chen, Shicai Yang, Di Xie, Shiliang Pu, Yueting Zhuang
• Abstract summary: 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 method. 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.
• Score: 64.49153847504141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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