Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
By Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li. Neural Information Processing Systems, 2019
313 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/e4a3d036bff96fe577b2bee2b9c48ad7f646041e
How researchers see this paper
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- Christian Simon
- Mehrtash Harandi
- Piotr Koniusz
- Richard Nock
- Taojiannan Yang
- Chen Chen
- Sijie Zhu
- Chenggang Clarence Yan
Citations in the corpus
- Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization (2020, methodology): “Our method with SAFA [1] has successfully retrieved the relevant satellite-view images.”
- University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization (2020, unknown): “We, therefore, do not conduct tricks, such as image alignment [28] or feature ensemble [25].”
- VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval (2020, methodology): “Recently, there has been a surge of interest in cross-view geo-localization [24, 22, 7, 17, 28, 21], which uses GPS-tagged aerial-view images as reference for street-view queries.”
- Adaptive Subspaces for Few-Shot Learning (2020, background): “Other works use feature attention modules [28, 29] to modulate features for few-shot learning [30, 31].”