Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

Related tags

Deep LearningLPN
Overview

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

Python 3.6 License: MIT

LPN

[Paper]

NEWs

Prerequisites

  • Python 3.6
  • GPU Memory >= 8G
  • Numpy > 1.12.1
  • Pytorch 0.3+
  • scipy == 1.2.1
  • [Optional] apex (for float16) Requirements & Quick Start

Getting started

Dataset & Preparation

Download University-1652 upon request. You may use the request template.

Or download CVUSA / CVACT.

For CVUSA, I follow the training/test split in (https://github.com/Liumouliu/OriCNN).

Train & Evaluation

Train & Evaluation University-1652

sh run.sh

Train & Evaluation CVUSA

python prepare_cvusa.py  
sh run_cvusa.sh

Train & Evaluation CVACT

python prepare_cvact.py  
sh run_cvact.sh

Citation

@article{wang2021LPN,
  title={Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization},
  author={Wang, Tingyu and Zheng, Zhedong and Yan, Chenggang and Zhang, jiyong and Sun, Yaoqi and Zheng, Bolun and Yang, Yi},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2021},
  publisher={IEEE},
  note={doi:{
    \href{http://dx.doi.org/10.1109/TCSVT.2021.3061265}{10.1109/TCSVT.2021.3061265}}}
}
@article{zheng2020university,
  title={University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization},
  author={Zheng, Zhedong and Wei, Yunchao and Yang, Yi},
  journal={ACM Multimedia},
  year={2020}
}

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