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
More filters
Posted Content
Generalizing from a Few Examples: A Survey on Few-Shot Learning
TL;DR: A thorough survey to fully understand Few-Shot Learning (FSL), and categorizes FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which used to reduce the size of the hypothesis space; and algorithm, which using prior knowledgeto alter the search for the best hypothesis in the given hypothesis space.
Proceedings ArticleDOI
Habitat: A Platform for Embodied AI Research
Manolis Savva,Jitendra Malik,Devi Parikh,Dhruv Batra,Abhishek Kadian,Oleksandr Maksymets,Yili Zhao,Erik Wijmans,Bhavana Jain,Julian Straub,Jia Liu,Vladlen Koltun +11 more
TL;DR: The comparison between learning and SLAM approaches from two recent works are revisited and evidence is found -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and the first cross-dataset generalization experiments are conducted.
Journal ArticleDOI
A Survey on Learning to Hash
TL;DR: In this paper, a comprehensive survey of the learning to hash algorithms is presented, categorizing them according to the manners of preserving the similarities into: pairwise similarity preserving, multi-wise similarity preservation, implicit similarity preserving and quantization, and discuss their relations.
Journal ArticleDOI
Deep Learning for Image Super-Resolution: A Survey
TL;DR: A survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way, which can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR.
Posted Content
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
TL;DR: In this paper, an unsupervised framework was proposed to learn a deep CNN for single view depth prediction without requiring a pre-training stage or annotated ground truth depths, by training the network in a manner analogous to an autoencoder.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
WordNet : an electronic lexical database
TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Principles of categorization
TL;DR: On those remote pages it is written that animals are divided into those that belong to the Emperor, and those that are trained, suckling pigs and stray dogs.
Proceedings ArticleDOI
Scalable Recognition with a Vocabulary Tree
David Nister,Henrik Stewenius +1 more
TL;DR: A recognition scheme that scales efficiently to a large number of objects and allows a larger and more discriminatory vocabulary to be used efficiently is presented, which it is shown experimentally leads to a dramatic improvement in retrieval quality.