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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CoCa: Contrastive Captioners are Image-Text Foundation Models
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Book ChapterDOI
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
Tien-Ju Yang,Andrew Howard,Bo Chen,Xiao Zhang,Alec Go,Mark Sandler,Vivienne Sze,Hartwig Adam +7 more
TL;DR: In this paper, the authors proposed an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget, which achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU.
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Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
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Peize Sun,Rufeng Zhang,Yi Jiang,Tao Kong,Chenfeng Xu,Wei Zhan,Masayoshi Tomizuka,Lei Li,Zehuan Yuan,Changhu Wang,Ping Luo +10 more
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Proceedings ArticleDOI
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