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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Proceedings ArticleDOI
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment
TL;DR: PANet as mentioned in this paper learns class-specific prototype representations from a few support images within an embedding space and then performs segmentation over the query images through matching each pixel to the learned prototypes.
Posted Content
Boosting Adversarial Attacks with Momentum
TL;DR: In this article, a broad class of momentum-based iterative algorithms to boost adversarial attacks is proposed to stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples.
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Monocular 3D Human Pose Estimation in the Wild Using Improved CNN Supervision
Dushyant Mehta,Helge Rhodin,Dan Casas,Pascal Fua,Oleksandr Sotnychenko,Weipeng Xu,Christian Theobalt +6 more
TL;DR: In this article, a CNN-based approach for 3D human body pose estimation from single RGB images is proposed to address the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data.
Proceedings ArticleDOI
COCO-Stuff: Thing and Stuff Classes in Context
TL;DR: COCO-Stuff as mentioned in this paper augments all 164k images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes, which leverages the original thing annotations.
Book ChapterDOI
Memory Aware Synapses: Learning What (not) to Forget
TL;DR: This paper argues that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively and proposes a novel approach for lifelong learning, coined Memory Aware Synapses (MAS), which computes the importance of the parameters of a neural network in an unsupervised and online manner.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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
ImageNet: A large-scale hierarchical image database
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
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