Drone-view target localization
6 papers with code • 1 benchmarks • 1 datasets
(Drone -> Satellite) Given one drone-view image or video, the task aims to find the most similar satellite-view image to localize the target building in the satellite view.
Benchmarks
These leaderboards are used to track progress in Drone-view target localization
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University-1652
Most implemented papers
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
layumi/University1652-Baseline
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To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i. e., drone-view target localization and drone navigation.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
wtyhub/LPN
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Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
Xuanmeng-Zhang/gnn-re-ranking
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We argue that the first phase equals building the k-nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph.
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
dmmm1997/fsra
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However it still has some limitations, e. g., it can only extract part of the information in the neighborhood and some scale reduction operations will make some fine-grained information lost.
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
AggMan96/RK-Net
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IEEE Transactions on Image Processing (TIP) 2022
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
Skyy93/Sample4Geo
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ICCV 2023
In this article, we present a simplified but effective architecture based on contrastive learning with symmetric InfoNCE loss that outperforms current state-of-the-art results.
