Computer Science > Computer Vision and Pattern Recognition
[Submitted on 18 Apr 2022 (v1), last revised 25 Feb 2024 (this version, v2)]
Title:Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
View PDF HTML (experimental)Abstract:Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. This task is mostly regarded as an image retrieval problem. The key underpinning this task is to design a series of deep neural networks to learn discriminative image descriptors. However, existing methods meet large performance drops under realistic weather, such as rain and fog, since they do not take the domain shift between the training data and multiple test environments into consideration. To minor this domain gap, we propose a Multiple-environment Self-adaptive Network (MuSe-Net) to dynamically adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network. As the name implies, the multiple-environment style extraction network is to extract the environment-related style information, while the self-adaptive feature extraction network utilizes an adaptive modulation module to dynamically minimize the environment-related style gap. Extensive experiments on two widely-used benchmarks, i.e., University-1652 and CVUSA, demonstrate that the proposed MuSe-Net achieves a competitive result for geo-localization in multiple environments. Furthermore, we observe that the proposed method also shows great potential to the unseen extreme weather, such as mixing the fog, rain and snow.
Submission history
From: Tingyu Wang [view email][v1] Mon, 18 Apr 2022 16:04:29 UTC (10,651 KB)
[v2] Sun, 25 Feb 2024 12:22:08 UTC (11,377 KB)
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