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Person re-identification has always been a hot and challenging task. Thispaper introduces our solution for the re-identification track in VIPriorsChallenge 2021. In this challenge, the difficulty is how to train the modelfrom scratch without any pretrained weight. In our method, we show usestate-of-the-art data processing strategies, model designs, and post-processingensemble methods, it is possible to overcome the difficulty of data shortageand obtain competitive results. (1) Both image augmentation strategy and novelpre-processing method for occluded images can help the model learn morediscriminative features. (2) Several strong backbones and multiple lossfunctions are used to learn more representative features. (3) Post-processingtechniques including re-ranking, automatic query expansion, ensemble learning,etc., significantly improve the final performance. The final score of our team(ALONG) is 96.5154% mAP, ranking first in the leaderboard.

A Technical Report for ICCV 2021 VIPriors Re-identification Challenge