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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Journal ArticleDOI
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein,Gustau Camps-Valls,Bjorn Stevens,Martin Jung,Joachim Denzler,Nuno Carvalhais,Nuno Carvalhais,Prabhat +7 more
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
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Globally and locally consistent image completion
TL;DR: This work presents a novel approach for image completion that results in images that are both locally and globally consistent, with a fully-convolutional neural network that can complete images of arbitrary resolutions by filling-in missing regions of any shape.
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Deep Visual-Semantic Alignments for Generating Image Descriptions
Andrej Karpathy,Li Fei-Fei +1 more
TL;DR: A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.
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Improved Baselines with Momentum Contrastive Learning
TL;DR: With simple modifications to MoCo, this note establishes stronger baselines that outperform SimCLR and do not require large training batches, and hopes this will make state-of-the-art unsupervised learning research more accessible.
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nuScenes: A multimodal dataset for autonomous driving
Holger Caesar,Varun Bankiti,Alex H. Lang,Sourabh Vora,Venice Erin Liong,Qiang Xu,Anush Krishnan,Yu Pan,Giancarlo Baldan,Oscar Beijbom +9 more
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Proceedings ArticleDOI
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