ImageNet Large Scale Visual Recognition Challenge View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-04-11

AUTHORS

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

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. More... »

PAGES

211-252

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Segmentation Propagation in ImageNet in COMPUTER VISION – ECCV 2012
  • 2009-09-09. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1996-06. Speed of processing in the human visual system in NATURE
  • 2014. Visualizing and Understanding Convolutional Networks in COMPUTER VISION – ECCV 2014
  • 2010. Image Classification Using Super-Vector Coding of Local Image Descriptors in COMPUTER VISION – ECCV 2010
  • 2001-05. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost in COMPUTER VISION – ECCV 2012
  • 2014-06-25. The Pascal Visual Object Classes Challenge: A Retrospective in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. Improving the Fisher Kernel for Large-Scale Image Classification in COMPUTER VISION – ECCV 2010
  • 2014. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition in COMPUTER VISION – ECCV 2014
  • 2013-04-02. Selective Search for Object Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012-09-05. Efficiently Scaling up Crowdsourced Video Annotation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Diagnosing Error in Object Detectors in COMPUTER VISION – ECCV 2012
  • 2007-10-31. LabelMe: A Database and Web-Based Tool for Image Annotation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Exact Acceleration of Linear Object Detectors in COMPUTER VISION – ECCV 2012
  • 2014. Microsoft COCO: Common Objects in Context in COMPUTER VISION – ECCV 2014
  • Journal

    TITLE

    International Journal of Computer Vision

    ISSUE

    3

    VOLUME

    115

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11263-015-0816-y

    DOI

    http://dx.doi.org/10.1007/s11263-015-0816-y

    DIMENSIONS

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    23 schema: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.
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