ImageNet Large Scale Visual Recognition Challenge
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11263_2015_Article_816.pdf
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15.95 MB
Format
Adobe PDF
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Author(s)
Russakovsky, Olga
Deng, Jia
Su, Hao
Krause, Jonathan
Satheesh, Sanjeev
Ma, Sean
Huang, Zhiheng
Karpathy, Andrej
Khosla, Aditya
Bernstein, Michael
Date Issued
April 2015
Journal
International Journal of Computer Vision
Publisher
Springer US
Citation
Russakovsky, Olga et al. “ImageNet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115.3 (2015): 211–252.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11263-015-0816-y
