In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how different components and design choices in a VG pipeline impact the final results, both in terms of performance (recall@N metric) and system requirements (such as execution time and memory consumption); ii) establish a systematic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing backbone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data augmentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in extraction time and dataset storage requirement. Code and trained models are available at https://deep-vg-bench.herokuapp.com/.

Deep Visual Geo-Localization Benchmark / Berton, GABRIELE MORENO; Mereu, Riccardo; Trivigno, Gabriele; Masone, Carlo; Csurka, Gabriela; Sattler, Torsten; Caputo, Barbara. - ELETTRONICO. - The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR):(2022), pp. 5396-5407. ((Intervento presentato al convegno The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) tenutosi a New Orleans nel Jun 19, 2022 – Jun 24, 2022.

Deep Visual Geo-Localization Benchmark

Gabriele Berton;Gabriele Trivigno;Carlo Masone;Barbara Caputo
2022

Abstract

In this paper, we propose a new open-source benchmarking framework for Visual Geo-localization (VG) that allows to build, train, and test a wide range of commonly used architectures, with the flexibility to change individual components of a geo-localization pipeline. The purpose of this framework is twofold: i) gaining insights into how different components and design choices in a VG pipeline impact the final results, both in terms of performance (recall@N metric) and system requirements (such as execution time and memory consumption); ii) establish a systematic evaluation protocol for comparing different methods. Using the proposed framework, we perform a large suite of experiments which provide criteria for choosing backbone, aggregation and negative mining depending on the use-case and requirements. We also assess the impact of engineering techniques like pre/post-processing, data augmentation and image resizing, showing that better performance can be obtained through somewhat simple procedures: for example, downscaling the images' resolution to 80% can lead to similar results with a 36% savings in extraction time and dataset storage requirement. Code and trained models are available at https://deep-vg-bench.herokuapp.com/.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2970714