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
Journal ArticleDOI
Prefrontal cortex as a meta-reinforcement learning system
Jane X. Wang,Zeb Kurth-Nelson,Dharshan Kumaran,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Demis Hassabis,Matthew Botvinick +7 more
TL;DR: A new theory is presented showing how learning to learn may arise from interactions between prefrontal cortex and the dopamine system, providing a fresh foundation for future research.
Book ChapterDOI
Grounding of Textual Phrases in Images by Reconstruction
TL;DR: A novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly, and demonstrates the effectiveness on the Flickr 30k Entities and ReferItGame datasets.
Book ChapterDOI
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Dima Damen,Hazel Doughty,Giovanni Maria Farinella,Sanja Fidler,Antonino Furnari,Evangelos Kazakos,Davide Moltisanti,Jonathan Munro,Toby Perrett,Will Price,Michael Wray +10 more
TL;DR: This paper introduces Open image in new window, a large-scale egocentric video benchmark recorded by 32 participants in their native kitchen environments and had the participants narrate their own videos (after recording), thus reflecting true intention, and crowd-sourced ground-truths based on these.
Journal ArticleDOI
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee,Heesung Kwon +1 more
TL;DR: In this article, a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification is proposed, which can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors.
Proceedings ArticleDOI
Image2StyleGAN++: How to Edit the Embedded Images?
TL;DR: A framework that combines embedding with activation tensor manipulation to perform high quality local edits along with global semantic edits on images and can restore high frequency features in images and thus significantly improves the quality of reconstructed images.
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.