ImageNet Large Scale Visual Recognition Challenge


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ImageNet Large Scale Visual Recognition Challenge

| created by semantic-scholar-bot | Crawl Semantic Scholar Open Research Corpus
Title
ImageNet Large Scale Visual Recognition Challenge
Type
Paper
Created
2015-01-01
Description
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.
Link
https://semanticscholar.org/paper/e74f9b7f8eec6ba4704c206b93bc8079af3da4bd
Identifier
DOI: 10.1007/s11263-015-0816-y

authors

created by Jia Deng
created by Hao Su
created by Sean Ma
created by Fei-Fei Li