Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice

Contemporary person re-identification (reid) methods usually require access to data from the deployment camera network during training in order to perform well. This is because contemporary reid 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 reid 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 reid 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 reid 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 reid 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 reid in practice. With a few lines of code, it increases the rank 1 reid accuracy by 11.8%, 33.2%, 12.8% and 8.5% on the VIPeR, PRID, GRID, and i-LIDS benchmarks respectively. Source codes are available at urlhttps://github.com/BJTUJia/personreIDDualNorm.