Hierarchical Attention Fusion for Geo-Localization

• URL: http://arxiv.org/abs/2102.09186v1
• Date: Thu, 18 Feb 2021 07:07:03 GMT
• Title: Hierarchical Attention Fusion for Geo-Localization
• Authors: Liqi Yan, Yiming Cui, Yingjie Chen, Dongfang Liu
• Abstract summary: We introduce a hierarchical attention fusion network using multi-scale features for geo-localization. We extract the hierarchical feature maps from a convolutional neural network (CNN) and organically fuse the extracted features for image representations. Our training is self-supervised using adaptive weights to control the attention of feature emphasis from each hierarchical level.
• Score: 7.544917072241684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geo-localization is a critical task in computer vision. In this work, we cast the geo-localization as a 2D image retrieval task. Current state-of-the-art methods for 2D geo-localization are not robust to locate a scene with drastic scale variations because they only exploit features from one semantic level for image representations. To address this limitation, we introduce a hierarchical attention fusion network using multi-scale features for geo-localization. We extract the hierarchical feature maps from a convolutional neural network (CNN) and organically fuse the extracted features for image representations. Our training is self-supervised using adaptive weights to control the attention of feature emphasis from each hierarchical level. Evaluation results on the image retrieval and the large-scale geo-localization benchmarks indicate that our method outperforms the existing state-of-the-art methods. Code is available here: \url{https://github.com/YanLiqi/HAF}.

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