Person re-identification has always been a hot and challenging task. This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, we show use state-of-the-art data processing strategies, model designs, and post-processing ensemble methods, it is possible to overcome the difficulty of data shortage and obtain competitive results. (1) Both image augmentation strategy and novel pre-processing method for occluded images can help the model learn more discriminative features. (2) Several strong backbones and multiple loss functions are used to learn more representative features. (3) Post-processing techniques including re-ranking, automatic query expansion, ensemble learning, etc., significantly improve the final performance. The final score of our team (ALONG) is 96.5154% mAP, ranking first in the leaderboard.
翻译：重新确定身份一直是一项艰巨而艰巨的任务。本文件介绍了我们在《2021年VIPrior挑战》中重新确定身份的解决方案。在这项挑战中,困难在于如何从零开始培训模型,而没有任何预先训练的重量。在我们的方法中,我们展示了使用最先进的数据处理战略、模型设计和后处理组合方法,有可能克服数据短缺的困难并获得竞争性结果。 (1) 图像增强战略和隐蔽图像的新型预处理方法都有助于模型学习更具有歧视性的特点。 (2) 使用几个强大的骨干和多重损失功能来学习更具代表性的特点。 (3) 后处理技术,包括重新排名、自动查询扩展、共同学习等,大大改进了最后的绩效。我们的团队(ALONG)最后得分是96.5154 % MAP,在领导板中排名第一。
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