Towards generalizable person re-identification with a bi-stream generative model

作者：

Highlights：

• We decouple the difficulties affecting the person re-identification task into the Camera-Camera (CC) problem and the Camera-Person (CP) problem.

• We propose a bi-stream generative model for solving the CC and CP problems separately, with promising results.

• We design a part-weighted loss based on the unbalanced number of human body parts in the dataset to guide the model to focus on the more important parts.

摘要

•We decouple the difficulties affecting the person re-identification task into the Camera-Camera (CC) problem and the Camera-Person (CP) problem.•We propose a bi-stream generative model for solving the CC and CP problems separately, with promising results.•We design a part-weighted loss based on the unbalanced number of human body parts in the dataset to guide the model to focus on the more important parts.

论文关键词：Person re-identification,Generalizable re-ID,Camera-Camera problem,Camera-Person problem

论文评审过程：Received 13 October 2021, Revised 28 July 2022, Accepted 1 August 2022, Available online 2 August 2022, Version of Record 6 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108954