@inproceedings{0fd98e36bb354250bc605c189bcd238f,
title = "Compact network training for person ReID",
abstract = "The task of person re-identification (ReID) has attracted growing attention in recent years leading to improved performance, albeit with little focus on real-world applications. Most SotA methods are based on heavy pre-trained models, e.g. ResNet50 (∼25M parameters), which makes them less practical and more tedious to explore architecture modifications. In this study, we focus on a small-sized randomly initialized model that enables us to easily introduce architecture and training modifications suitable for person ReID. The outcomes of our study are a compact network and a fitting training regime. We show the robustness of the network by outperforming the SotA on both Market1501 and DukeMTMC. Furthermore, we show the representation power of our ReID network via SotA results on a different task of multi-object tracking.",
keywords = "Compact network, Deep person reid, Multi-object tracking",
author = "Hussam Lawen and Avi Ben-Cohen and Matan Protter and Itamar Friedman and Lihi Zelnik-Manor",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 10th ACM International Conference on Multimedia Retrieval, ICMR 2020 ; Conference date: 08-06-2020 Through 11-06-2020",
year = "2020",
month = jun,
day = "8",
doi = "https://doi.org/10.1145/3372278.3390686",
language = "الإنجليزيّة",
series = "ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval",
pages = "164--171",
booktitle = "ICMR 2020 - Proceedings of the 2020 International Conference on Multimedia Retrieval",
}