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Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
[article]
2020
arXiv
pre-print
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part
arXiv:2004.10547v2
fatcat:dvcljt7c7ratvpooe6nrmj3nle