Have Fun Storming the Castle(s)!

Connor Anderson, Adam Teuscher, Elizabeth Anderson, Alysia Larsen, Josh Shirley, Ryan Farrell
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引用次数: 3

Abstract

In recent years, large-scale datasets, each typically tailored to a particular problem, have become a critical factor towards fueling rapid progress in the field of computer vision. This paper describes a valuable new dataset that should accelerate research efforts on problems such as fine-grained classification, instance recognition and retrieval, and geolocalization. The dataset, comprised of more than 2400 individual castles, palaces and fortresses from more than 90 countries, contains more than 770K images in total. This paper details the dataset’s construction process, the characteristics including annotations such as location (geotagged latlong and country label), construction date, Google Maps link and estimated per-class and per-image difficulty. An experimental section provides baseline experiments for important vision tasks including classification, instance retrieval and geolocalization (estimating global location from an image’s visual appearance). The dataset is publicly available at vision.cs.byu.edu/castles.
玩得开心攻占城堡!
近年来,大型数据集(每个数据集通常针对一个特定的问题)已成为推动计算机视觉领域快速发展的关键因素。本文描述了一个有价值的新数据集,该数据集应该加速诸如细粒度分类,实例识别和检索以及地理定位等问题的研究工作。该数据集由来自90多个国家的2400多个城堡、宫殿和堡垒组成,总共包含超过77万张图像。本文详细介绍了数据集的构建过程,包括位置(地理标记的latlong和国家标签)、构建日期、谷歌地图链接以及估计的每个类和每个图像的难度等注释。实验部分为重要的视觉任务提供了基线实验,包括分类、实例检索和地理定位(从图像的视觉外观估计全局位置)。该数据集可在vision.cs.byu.edu/castles上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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