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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Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
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A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture
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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek,Grégoire Montavon,Sebastian Lapuschkin,Christopher J. Anders,Klaus-Robert Müller +4 more
TL;DR: In this paper, the authors provide a timely overview of explainable AI, with a focus on 'post-hoc' explanations, explain its theoretical foundations, and put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations.
Proceedings ArticleDOI
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Andy Zeng,Shuran Song,Kuan-Ting Yu,Elliott Donlon,Francois Robert Hogan,Maria Bauza,Daolin Ma,Orion Taylor,Melody Liu,Eudald Romo,Nima Fazeli,Ferran Alet,Nikhil Chavan Dafle,Rachel Holladay,Isabella Morena,Prem Qu Nair,Druck Green,Ian Taylor,Weber Liu,Thomas Funkhouser,Alberto Rodriguez +20 more
TL;DR: In this article, a multi-affordance grasping algorithm was used to select and execute four different grasping primitive behaviors for both known and novel grasped objects in a cluttered environment, and a cross-domain image classification framework was proposed to match observed images to product images.
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Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
TL;DR: Contrast prior is utilized, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information and is integrated with RGB features for SOD, using a novel fluid pyramid integration.
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