@inproceedings{b1a194fde93e491b80d8a83fa6c595cc,
title = "PARTITION AND REUNION: A VIEWPOINT-AWARE LOSS FOR VEHICLE RE-IDENTIFICATION",
abstract = "Vehicle Re-Identification (ReID) aims to retrieve images of vehicles with the same identity from different scenarios. It is a challenging task due to the large intra-identity discrepancy caused by viewpoint variations and the subtle inter-identity difference produced by similar appearances. In this paper, we propose a Viewpoint-Aware Loss (VAL) function to deal with these challenges. Specifically, we propose partition and reunion operations in VAL, which significantly shrinks the intra-identity distance and acquires viewpoint-invariant representations. In addition, we embed a multi-decision boundary mechanism in VAL. It contributes to enlarging the inter-identity distance. A comprehensive evaluation on two benchmarks shows the superiority of our method in contrast to a series of existing state-of-the-arts.",
keywords = "Vehicle re-identification, loss function, representation learning, viewpoint-aware",
author = "Haobo Chen and Yang Liu and Yang Huang and Wei Ke and Hao Sheng",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Image Processing, ICIP 2022 ; Conference date: 16-10-2022 Through 19-10-2022",
year = "2022",
doi = "10.1109/ICIP46576.2022.9897541",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2246--2250",
booktitle = "2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings",
address = "United States",
}