Abstract
Contemporary person re-identification (Re-ID) methods usually require access to data from the deployment camera network during training in order to perform well. This is because contemporary Re-ID models trained on one dataset do not generalise to other camera networks due to the domain-shift between datasets. This requirement is often the bottleneck for deploying Re-ID systems in practical security or commercial applications as it may be impossible to collect this data in advance or prohibitively costly to annotate it. This paper alleviates this issue by proposing a simple baseline for domain generalizable~(DG) person re-identification. That is, to learn a Re-ID model from a set of source domains that is suitable for application to unseen datasets out-of-the-box, without any model updating. Specifically, we observe that the domain discrepancy in Re-ID is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep Re-ID models. Instance Normalization~(IN) in early layers filters out style statistic variations and Feature Normalization~(FN) in deep layers is able to further eliminate disparity in content statistics. Compared to contemporary alternatives, this approach is extremely simple to implement, while being faster to train and test, thus making it an extremely valuable baseline for implementing Re-ID in practice. With a few lines of code, it increases the rank 1 Re-ID accuracy by 11.7\%, 28.9\%, 10.1\% and 6.3\% on the VIPeR, PRID, GRID, and i-LIDS benchmarks respectively. Source code will be made available.
Abstract (translated)
现代的人再识别（Re-ID）方法通常需要在训练期间访问部署摄像头网络中的数据，以便表现良好。这是因为在一个数据集上训练的现代RE-ID模型由于数据集之间的域转换而不通用于其他摄像机网络。这一需求通常是在实际安全或商业应用中部署REID系统的瓶颈，因为可能无法提前收集这些数据，或者注释这些数据的成本过高。本文通过提出一个简单的域可推广~（dg）人再识别基线，来缓解这一问题。也就是说，要从一组源域中学习REID模型，该源域适合应用程序在不更新任何模型的情况下从框中看不到数据集。具体来说，我们观察到REID中的域差异是由于数据集之间的样式和内容差异造成的，并且证明了适当的实例和特征规范化可以减轻深层REID模型中产生的域变化。实例归一化（in）可以滤除早期层次的风格统计变化，而深层层次的特征归一化（fn）可以进一步消除内容统计中的差异。与当代的替代方法相比，这种方法非常容易实现，同时训练和测试速度更快，因此在实践中实现REID成为非常有价值的基线。通过几行代码，它将Viper、Prid、Grid和I-Lids基准的排名1 RE ID精度分别提高了11.7%、28.9%、10.1%和6.3%。源代码将可用。
URL
https://arxiv.org/abs/1905.03422
