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Learning Quintuplet Loss for Large-scale Visual Geo-Localization
[article]
2020
arXiv
pre-print
With the maturity of Artificial Intelligence (AI) technology, Large Scale Visual Geo-Localization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geo-location of a given query image. The main challenge of LSVGL faced by many experiments due to the appearance of real-word places may differ in various ways. While perspective deviation almost inevitably exists between training images and query images because of the arbitrary
arXiv:1907.11350v2
fatcat:owkmdcsrarclvp6ppywuy2knma