  Person re-identification (re-ID) aims to identify the same person of interest
across non-overlapping capturing cameras, which plays an important role in
visual surveillance applications and computer vision research areas. Fitting a
robust appearance-based representation extractor with limited collected
training data is crucial for person re-ID due to the high expanse of annotating
the identity of unlabeled data. In this work, we propose a Stronger Baseline
for person re-ID, an enhancement version of the current prevailing method,
namely, Strong Baseline, with tiny modifications but a faster convergence rate
and higher recognition performance. With the aid of Stronger Baseline, we
obtained the third place (i.e., 0.94 in mAP) in 2021 VIPriors Re-identification
Challenge without the auxiliary of ImageNet-based pre-trained parameter
initialization and any extra supplemental dataset.
