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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Proceedings ArticleDOI
The Visual Object Tracking VOT2017 Challenge Results
Matej Kristan,Ales Leonardis,Jiri Matas,Michael Felsberg,Roman Pflugfelder,Luka Čehovin Zajc,Tomas Vojir,Gustav Häger,Alan Lukezic,Abdelrahman Eldesokey,Gustavo Fernandez,Alvaro Garcia-Martin,Andrej Muhič,Alfredo Petrosino,Alireza Memarmoghadam,Andrea Vedaldi,Antoine Manzanera,Antoine Tran,A. Aydin Alatan,Bogdan Mocanu,Boyu Chen,Chang Huang,Changsheng Xu,Chong Sun,Dalong Du,David Zhang,Dawei Du,Deepak Mishra,Erhan Gundogdu,Erhan Gundogdu,Erik Velasco-Salido,Fahad Shahbaz Khan,Francesco Battistone,Gorthi R. K. Sai Subrahmanyam,Goutam Bhat,Guan Huang,Guilherme Sousa Bastos,Guna Seetharaman,Hongliang Zhang,Houqiang Li,Huchuan Lu,Isabela Drummond,Jack Valmadre,Jae-chan Jeong,Jaeil Cho,Jae-Yeong Lee,Jana Noskova,Jianke Zhu,Jin Gao,Jingyu Liu,Ji-Wan Kim,João F. Henriques,José M. Martínez,Junfei Zhuang,Junliang Xing,Junyu Gao,Kai Chen,Kannappan Palaniappan,Karel Lebeda,Ke Gao,Kris M. Kitani,Lei Zhang,Lijun Wang,Lingxiao Yang,Longyin Wen,Luca Bertinetto,Mahdieh Poostchi,Martin Danelljan,Matthias Mueller,Mengdan Zhang,Ming-Hsuan Yang,Nianhao Xie,Ning Wang,Ondrej Miksik,Payman Moallem,Pallavi Venugopal M,Pedro Senna,Philip H. S. Torr,Qiang Wang,Qifeng Yu,Qingming Huang,Rafael Martin-Nieto,Richard Bowden,Risheng Liu,Ruxandra Tapu,Simon Hadfield,Siwei Lyu,Stuart Golodetz,Sunglok Choi,Tianzhu Zhang,Titus Zaharia,Vincenzo Santopietro,Wei Zou,Weiming Hu,Wenbing Tao,Wenbo Li,Wengang Zhou,Xianguo Yu,Xiao Bian,Yang Li,Yifan Xing,Yingruo Fan,Zheng Zhu,Zhipeng Zhang,Zhiqun He +104 more
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Book ChapterDOI
Ambient Sound Provides Supervision for Visual Learning
Andrew Owens,Jiajun Wu,Josh H. McDermott,William T. Freeman,William T. Freeman,Antonio Torralba +5 more
TL;DR: This work trains a convolutional neural network to predict a statistical summary of the sound associated with a video frame, and shows that this representation is comparable to that of other state-of-the-art unsupervised learning methods.
Journal ArticleDOI
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
TL;DR: A survey of the role of visual analytics in deep learning research is presented, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How.
Posted Content
What do we need to build explainable AI systems for the medical domain
TL;DR: It is argued that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitates transparency and trust.
Journal ArticleDOI
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova,Hassan Rom,Neil Alldrin,Jasper Uijlings,Ivan Krasin,Jordi Pont-Tuset,Shahab Kamali,Stefan Popov,Matteo Malloci,Alexander Kolesnikov,Tom Duerig,Vittorio Ferrari +11 more
TL;DR: Open Images V4 as mentioned in this paper is a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection from Flickr without a predefined list of class names or tags.
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.