Multiple-environment Self-adaptive Network for aerial-view geo-localization

Author:

Wang Tingyu,Zheng Zhedong,Sun Yaoqi,Yan Chenggang,Yang Yi,Chua Tat-Seng

Funder

Zhejiang Province Natural Science Foundation

Publisher

Elsevier BV

Reference54 articles.

1. University-1652: A multi-view multi-source benchmark for drone-based geo-localization;Zheng,2020

2. Each part matters: Local patterns facilitate cross-view geo-localization;Wang;IEEE Trans. Circuits Syst. Video Technol.,2022

3. Joint representation learning and keypoint detection for cross-view geo-localization;Lin;IEEE Trans. Image Process.,2022

4. L. Liu, H. Li, Lending orientation to Neural Networks for Cross-view Geo-localization, in: IEEE Conference on Computer Vision and Pattern Recognition, 2019.

5. Y. Shi, X. Yu, L. Liu, T. Zhang, H. Li, Optimal Feature Transport for Cross-View Image Geo-Localization, in: AAAI Conference on Artificial Intelligence, 2020.

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2. Aerial-view geo-localization based on multi-layer local pattern cross-attention network;Applied Intelligence;2024-09-03

3. UAV Visual Localization Based on SFA-Resnet and Contrastive Learning;2024 9th International Conference on Electronic Technology and Information Science (ICETIS);2024-05-17

4. Learning Cross-View Visual Geo-Localization Without Ground Truth;IEEE Transactions on Geoscience and Remote Sensing;2024

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