This repo gives the code for the paper "Xinchen Liu, Wu Liu, Jinkai Zheng, Chenggang Yan, Tao Mei: Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification. ACM MM 2020".
This repo gives the code for the paper "Xinchen Liu, Wu Liu, Jinkai Zheng, Chenggang Yan, Tao Mei: Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification. ACM MM 2020". This code is based on reid strong baseline.
pip install tensorboard
To train a vehicle reid model with parsing, you need the original image datasets like VeRi and the parsing masks of all images. For vehicle parsing models pretrained on the MVP dataset based on PSPNet/DeepLabV3/HRNet, please refer to this repo.
You can run the examplar training script in .sh
files.
The main code for GCN can be found in
root
engine
trainer_selfgcn.py # training pipline
modeling
baseline_selfgcn.py # definition of the model
tools
train_selfgcn.py # training preparation
The code for data io and sampler also be modified for the parsing based reid method.
PCRNet is released under the Apache 2.0 license.
@inproceedings{mm/LiuLZY020,
author = {Xinchen Liu and
Wu Liu and
Jinkai Zheng and
Chenggang Yan and
Tao Mei},
title = {Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle
Re-identification},
booktitle = {ACM MM},
pages = {907--915},
year = {2020}
}