Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- pp 248-255
TLDR
A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.Abstract:
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.read more
Citations
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Proceedings ArticleDOI
Learning from Simulated and Unsupervised Images through Adversarial Training
TL;DR: SimGAN as mentioned in this paper uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors, and achieves state-of-the-art results on the MPIIGaze dataset without any labeled real data.
Proceedings ArticleDOI
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini,David Wagner +1 more
TL;DR: In this paper, the authors survey ten recent proposals for adversarial examples and compare their efficacy, concluding that all can be defeated by constructing new loss functions, and propose several simple guidelines for evaluating future proposed defenses.
Journal ArticleDOI
Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
TL;DR: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections by proposing a simple and efficient alternating minimization algorithm, dubbed iterative quantization (ITQ), and demonstrating an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.
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Understanding Neural Networks Through Deep Visualization
TL;DR: This work introduces several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations of convolutional neural networks.
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Microsoft COCO Captions: Data Collection and Evaluation Server
Xinlei Chen,Hao Fang,Tsung-Yi Lin,Ramakrishna Vedantam,Saurabh Gupta,Piotr Dollár,C. Lawrence Zitnick +6 more
TL;DR: The Microsoft COCO Caption dataset and evaluation server are described and several popular metrics, including BLEU, METEOR, ROUGE and CIDEr are used to score candidate captions.
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