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
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.
David Tellez,Geert Litjens,Péter Bándi,Wouter Bulten,John-Melle Bokhorst,Francesco Ciompi,Jeroen van der Laak +6 more
TL;DR: In this article, the authors compared stain color augmentation and normalization techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories.
Proceedings ArticleDOI
Convolutional feature masking for joint object and stuff segmentation
Jifeng Dai,Kaiming He,Jian Sun +2 more
TL;DR: Wang et al. as mentioned in this paper proposed to exploit shape information via masking convolutional features, where the proposal segments (e.g., super-pixels) are treated as masks on the CNN feature maps and used to train classifiers for recognition.
Proceedings ArticleDOI
DPOD: 6D Pose Object Detector and Refiner
TL;DR: A novel deep learning method that estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models and demonstrates that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement.
Book ChapterDOI
Human pose estimation via Convolutional Part Heatmap Regression
TL;DR: A CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions is proposed, and achieves top performance on the MPII and LSP data sets.
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Attention Residual Learning for Skin Lesion Classification
TL;DR: The results indicate that the proposed ARL-CNN model can adaptively focus on the discriminative parts of skin lesions, and thus achieve the state-of-the-art performance in skin lesion classification.
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