ImageNet Large Scale Visual Recognition Challenge

@article{Russakovsky2015ImageNetLS,
  title={ImageNet Large Scale Visual Recognition Challenge},
  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 Li Fei-Fei},
  journal={International Journal of Computer Vision},
  year={2015},
  volume={115},
  pages={211-252}
}
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. [...] Key Result We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.Expand
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References

SHOWING 1-10 OF 137 REFERENCES
The Pascal Visual Object Classes (VOC) Challenge
TLDR
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse. Expand
Learning Deep Features for Scene Recognition using Places Database
TLDR
A new scene-centric database called Places with over 7 million labeled pictures of scenes is introduced with new methods to compare the density and diversity of image datasets and it is shown that Places is as dense as other scene datasets and has more diversity. Expand
Caltech-256 Object Category Dataset
We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examplesExpand
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
SUN database: Large-scale scene recognition from abbey to zoo
TLDR
This paper proposes the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images and uses 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. Expand
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TLDR
This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Expand
Large-scale image classification: Fast feature extraction and SVM training
TLDR
A parallel averaging stochastic gradient descent (ASGD) algorithm for training one-against-all 1000-class SVM classifiers and a Hadoop scheme that performs feature extraction in parallel using hundreds of mappers, which achieves state-of-the-art performance on the ImageNet 1000- class classification. Expand
The Pascal Visual Object Classes Challenge: A Retrospective
TLDR
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges. Expand
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
TLDR
For certain classes that are particularly prevalent in the dataset, such as people, this work is able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors. Expand
Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of sceneExpand
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