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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
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TLDR
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.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 five years of the challenge, and propose future directions and improvements.read more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: 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.
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Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
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DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Forrest Iandola,Matthew W. Moskewicz,Sergey Karayev,Ross Girshick,Trevor Darrell,Kurt Keutzer +5 more
TL;DR: DenseNet is presented, an open source system that computes dense, multiscale features from the convolutional layers of a CNN based object classifier.
Book ChapterDOI
Diagnosing error in object detectors
TL;DR: This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives, and shows that sensitivity to size, localization error, and confusion with similar objects are the most impactful forms of error.
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Return of the Devil in the Details: Delving Deep into Convolutional Nets
TL;DR: In this paper, the authors conduct a rigorous evaluation of different deep architectures and compare them on a common ground, identifying and disclosing important implementation details, and also identify aspects of deep and shallow methods that can be successfully shared.
Journal ArticleDOI
Nonparametric Scene Parsing via Label Transfer
TL;DR: This paper proposes a novel, nonparametric approach for object recognition and scene parsing using a new technology the authors name label transfer, which is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
Proceedings ArticleDOI
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
TL;DR: Locality-sensitive hashing as discussed by the authors replaces the dot-product kernel operator in the convolution with a fixed number of hash-table probes that effectively sample all the filter responses in time independent of the size of the filter bank.