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
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
Amaia Salvador,Nicholas Hynes,Yusuf Aytar,Javier Marin,Ferda Ofli,Ingmar Weber,Antonio Torralba +6 more
TL;DR: This paper introduces Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images, and demonstrates that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic.
Journal ArticleDOI
Deep image mining for diabetic retinopathy screening.
TL;DR: In this article, a generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps, showing which pixels in images play a role in the image-level predictions.
Journal ArticleDOI
Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network
TL;DR: A novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks and outperforms the state-of-the-art methods greatly in learning quality and learning speed.
Proceedings ArticleDOI
RON: Reverse Connection with Objectness Prior Networks for Object Detection
TL;DR: RON as mentioned in this paper proposes a reverse connection to detect objects on multi-levels of CNNs, which reduces the searching space of objects by optimizing the reverse connection, objectness prior and object detector jointly by a multi-task loss function.
Posted Content
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
TL;DR: It is shown that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, followed by training a linear classifier on top of this representation, outperforms state-of-the-art few-shot learning methods.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
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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
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