HIERARCHICAL ATTENTION FUSION FOR GEO-LOCALIZATION
February 25, 2021 ยท View on GitHub
Introduction
HAF extracts the multi-scale feature maps from a convolutional neural network (CNN) to perform hierarchical attention fusion for image representations. This repo is the PyTorch implementation of ICASSP2021 paper "HIERARCHICAL ATTENTION FUSION FOR GEO-LOCALIZATION"
[pdf]
Installation
Please find detailed steps Here for installation and dataset preparation.
Train & Test
Please find details Here for step-by-step instructions.
Model Zoo
Please refer to Here for trained models.
Inference on a single image
Please refer to Here for inference on a single image.
Train on customized dataset
Please refer to Here to prepare your own dataset.
License
HAF is released under the MIT license.
Citation
If you find this repo useful for your research, please consider citing the paper
@inproceedings{yan2021densernet,
title={Hierarchical Attention Fusion for Geo-localization},
author={Liqi Yan, Yiming Cui, Yingjie Chen, Dongfang Liu},
booktitle={ICASSP}
year={2021},
}
Acknowledgements
We truely thanksful of the following piror efforts in terms of knowledge contributions and open-source repos. Particularly, "ASLFeat" has a similar approach to ours but using strong supervision.
- NetVLAD: CNN architecture for weakly supervised place recognition (CVPR'16) [paper] [official code (pytorch-NetVlad)]
- SARE: Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization (ICCV'19) [paper] [official code (MatConvNet)]
- ASLFeat: Learning Local Features of Accurate Shape and Localization (CVPR'20) [paper] [official code]