Deep Visual Geo-Localization Benchmark
| العنوان: | Deep Visual Geo-Localization Benchmark |
|---|---|
| المؤلفون: | Gabriele Berton, Riccardo Mereu, Gabriele Trivigno, Carlo Masone, Gabriela Csurka, Torsten Sattler, Barbara Caputo |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, visual place recognition, Visual geo-localization, image retrieval, computer vision, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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/. Comment: CVPR 2022 (Oral) |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cab5a0d5c0e9316a7916d801287104b http://hdl.handle.net/11583/2970714 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....2cab5a0d5c0e9316a7916d801287104b |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |