A Technical Report for ICCV 2021 VIPriors Re-identification Challenge

• URL: http://arxiv.org/abs/2109.15164v1
• Date: Thu, 30 Sep 2021 14:29:31 GMT
• Title: A Technical Report for ICCV 2021 VIPriors Re-identification Challenge
• Authors: Cen Liu, Yunbo Peng, Yue Lin
• Abstract summary: This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. It shows use state-of-the-art data processing strategies, model designs, and post-processing ensemble methods. The final score of our team (ALONG) is 96.5154% mAP, ranking first in the leaderboard.
• Score: 5.940699390639281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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