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 for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Pranav Rajpurkar,Jeremy Irvin,Robyn L. Ball,Kaylie Zhu,Brandon Yang,Hershel Mehta,Tony Duan,Daisy Ding,Aarti Bagul,Curtis P. Langlotz,Bhavik N. Patel,Kristen W. Yeom,Katie Shpanskaya,Francis G. Blankenberg,Jayne Seekins,Timothy J. Amrhein,David A. Mong,Safwan Halabi,Evan J. Zucker,Andrew Y. Ng,Matthew P. Lungren +20 more
TL;DR: CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies.
Journal ArticleDOI
Towards better exploiting convolutional neural networks for remote sensing scene classification
TL;DR: An analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors points that fine tuning tends to be the best performing strategy.
Proceedings ArticleDOI
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
TL;DR: A scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task, is proposed that can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.
Posted Content
Regularized Evolution for Image Classifier Architecture Search
TL;DR: This work evolves an image classifier---AmoebaNet-A---that surpasses hand-designs for the first time and gives evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search.
Journal ArticleDOI
BabyTalk: Understanding and Generating Simple Image Descriptions
Girish Kulkarni,Visruth Premraj,Vicente Ordonez,Sagnik Dhar,Siming Li,Yejin Choi,Alexander C. Berg,Tamara L. Berg +7 more
TL;DR: The proposed system to automatically generate natural language descriptions from images is very effective at producing relevant sentences for images and generates descriptions that are notably more true to the specific image content than previous work.
References
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Proceedings ArticleDOI
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