In this article, we study the problem of detecting objects on geolocation to compare images from different points of view. Inspired by the human visual system for recognizing local patterns, we offer a new structure called RK-NET to study the discriminative representation and detect characteristic key points using a single network. In particular, we represent the attention module (USAM), which can automatically detect representative key points on the map maps. USAM gives a significant improvement in performance and can be easily connected to various CV algorithms. With the help of extensive experiments, we demonstrate that by turning on USAM RK-NET, training models without additional resources is accelerated. Learning to present and detect key points are two closely related tasks. Usam is easy to introduce, and it can be integrated with existing methods, additionally improving the work of any models. We achieve competitive accuracy when working with geolocations on three complex data sets: University-1652, CVUSA and CVACT. The code is available at: .

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#machinelearning #articalIlinTelling #ai #DatAstascience #PROGRAMMMING #TECHNOLOGY #DEEPLEARNING #BigDATA #Computerscience