J
Jonathan Krause
Researcher at Google
Publications - 48
Citations - 45410
Jonathan Krause is an academic researcher from Google. The author has contributed to research in topics: Object detection & Diabetic retinopathy. The author has an hindex of 27, co-authored 45 publications receiving 30925 citations. Previous affiliations of Jonathan Krause include California Institute of Technology & Stanford University.
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Journal ArticleDOI
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
TL;DR: 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.
Journal Article
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Michael S. Bernstein,Li Fei-Fei,Alexander C. Berg,Aditya Khosla +11 more
Proceedings ArticleDOI
3D Object Representations for Fine-Grained Categorization
TL;DR: This paper lifts two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location, and shows their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.
Posted Content
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
TL;DR: The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.
Proceedings ArticleDOI
Fine-grained recognition without part annotations
TL;DR: This work proposes a method for fine-grained recognition that uses no part annotations, based on generating parts using co-segmentation and alignment, which is combined in a discriminative mixture.