ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TLDR
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.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.read more
Citations
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Born Again Neural Networks
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REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
José Ignacio Orlando,Huazhu Fu,João Barbossa Breda,Karel Van Keer,Deepti R. Bathula,Andres Diaz-Pinto,Ruogu Fang,Pheng-Ann Heng,Jeyoung Kim,Joon-Ho Lee,Joonseok Lee,Xiaoxiao Li,Peng Liu,Shuai Lu,Balamurali Murugesan,Valery Naranjo,Sai Samarth R. Phaye,Sharath M Shankaranarayana,Apoorva Sikka,Jaemin Son,Anton van den Hengel,Shujun Wang,Junyan Wu,Zifeng Wu,Guanghui Xu,Yongli Xu,Pengshuai Yin,Fei Li,Xiulan Zhang,Yanwu Xu,Hrvoje Bogunovic +30 more
TL;DR: It is observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task, and the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.
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End-to-End Blind Image Quality Assessment Using Deep Neural Networks
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References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
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TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
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