Stronger Baseline for Person Re-Identification

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.

arXiv commentThe third-place solution for ICCV2021 VIPriors Re-identification Challenge

References in corpus (6)

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• Person Re-identification: Past, Present and Future
• AutoAugment: Learning Augmentation Policies from Data
• Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification
• HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning