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]
Please find detailed steps Here for installation and dataset preparation.
Please find details Here for step-by-step instructions.
Please refer to Here for trained models.
Please refer to Here for inference on a single image.
Please refer to Here to prepare your own dataset.
HAF is released under the MIT license.
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},
}
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]