ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
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DOI：
10.1007/s11263-015-0816-y
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发表时间：
2015-12-01
影响因子：
19.5
通讯作者：
Fei-Fei, Li
中科院分区：
文献类型：
--
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
ImageNet大规模视觉识别挑战赛是对数百个对象类别和数百万张图像进行对象类别分类和检测的基准。从2010年至今，该挑战赛每年举办一次，吸引了50多个机构的参与。本文介绍了这个基准数据集的创建和对象识别的进展，已经成为可能。我们讨论了收集大规模地面实况注释的挑战，强调了分类对象识别的关键突破，详细分析了大规模图像分类和对象检测领域的现状，并比较了最先进的计算机视觉精度与人类精度。最后，我们总结了5年来的经验教训，并提出了未来的发展方向和改进措施。
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.