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arXiv · 2112.01059

Stronger Baseline for Person Re-Identification

Fengliang Qi·Bo Yan·Leilei Cao·Hongbin Wang
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Abstract

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.

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Fengliang Qi, Bo Yan, Leilei Cao, Hongbin Wang. 2021-12-02. Stronger Baseline for Person Re-Identification. https://arxiv.org/abs/2112.01059

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