@inproceedings{56d1aa8ed00e4ff1ad7ec448ecd0255b,
title = "An Orthogonal Fusion of Local and Global Features for Drone-based Geo-localization",
abstract = "Drone-based geo-localization is an image retrieval task which is the foundation of many drone-based multimedia applications, such as object detection, drone navigation and mapping. It is challenging due to the large visual appearance changes caused by viewpoint variation and time misalignment. Existing methods primarily focus on global representation embedding while disregarding the local features. We propose a CNN-based model containing a global and a local branch to extract features in these two perspectives and then features are subsequently aggregated by orthogonal fusion. We achieve competitive results on University-1652/160k datasets among the ViT-based state-of-the-art models. Experimental and qualitative results that validate the effectiveness of our solution are also shown.",
keywords = "deep learning, drone, geo-localization, image retrieval",
author = "Tian Zhan and Cheng Zhang and Sibo You and Kai Sun and Di Su",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective, UAVM 2023 ; Conference date: 02-11-2023",
year = "2023",
month = nov,
day = "2",
doi = "10.1145/3607834.3616564",
language = "English",
series = "UAVM 2023 - Proceedings of the 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective, Co-located with MM 2023",
publisher = "Association for Computing Machinery, Inc",
pages = "1--6",
booktitle = "UAVM 2023 - Proceedings of the 2023 Workshop on UAVs in Multimedia",
}