2015 ImageNetLargeScaleVisualRecogni
- (Russakovsky et al., 2015) ⇒ Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. (2015). "ImageNet Large Scale Visual Recognition Challenge". In: International Journal of Computer Vision (IJCV). DOI:10.1007/s11263-015-0816-y.
Subject Headings: ImageNet Task, ImageNet Dataset.
Notes
Cited By
- Google Scholar: ~ 19,977 Citations, Retrieved: 2020-12-12.
Quotes
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 five years of the challenge, and propose future directions and improvements.
References
BibTeX
@article{2015_ImageNetLargeScaleVisualRecogni,
author = {Olga Russakovsky and
Jia Deng and
Hao Su and
Jonathan Krause and
Sanjeev Satheesh and
Sean Ma and
Zhiheng Huang and
Andrej Karpathy and
Aditya Khosla and
Michael S. Bernstein and
Alexander C. Berg and
Fei-Fei Li},
title = {ImageNet Large Scale Visual Recognition Challenge},
journal = {Int. J. Comput. Vis.},
volume = {115},
number = {3},
pages = {211--252},
year = {2015},
url = {https://doi.org/10.1007/s11263-015-0816-y},
doi = {10.1007/s11263-015-0816-y},
}