A Technical Report for ICCV 2021 VIPriors Re-identification Challenge

Person re-identification has always been a hot and challenging task. This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, we show use state...

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Bibliographic Details
Published inarXiv.org
Main Authors Liu, Cen, Peng, Yunbo, Lin, Yue
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.09.2021
Subjects
Business competition
Data processing
Post-processing
Query expansion
Query processing
Ranking
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Summary:Person re-identification has always been a hot and challenging task. This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, we show use state-of-the-art data processing strategies, model designs, and post-processing ensemble methods, it is possible to overcome the difficulty of data shortage and obtain competitive results. (1) Both image augmentation strategy and novel pre-processing method for occluded images can help the model learn more discriminative features. (2) Several strong backbones and multiple loss functions are used to learn more representative features. (3) Post-processing techniques 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.
ISSN:2331-8422