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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Channel Pruning for Accelerating Very Deep Neural Networks
Yihui He,Xiangyu Zhang,Jian Sun +2 more
TL;DR: In this paper, a LASSO regression based channel selection and least square reconstruction is proposed to accelerate very deep convolutional neural networks, which achieves 5× speedup along with only 0.3% increase of error.
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Regularized Evolution for Image Classifier Architecture Search
TL;DR: AmoebaNet-A as mentioned in this paper modified the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes and achieved state-of-the-art performance.
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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob,Skirmantas Kligys,Bo Chen,Menglong Zhu,Matthew Tang,Andrew Howard,Hartwig Adam,Dmitry Kalenichenko +7 more
TL;DR: A quantization scheme is proposed that allows inference to be carried out using integer- only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware.
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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.
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Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
TL;DR: A simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent.
References
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Proceedings ArticleDOI
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