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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Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes,Maayan Frid-Adar,Hayit Greenspan,Patrick D. Browning,Huangqi Zhang,Wenbin Ji,Adam Bernheim,Eliot L. Siegel +7 more
TL;DR: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus demonstrate they can differentiate coronavirus patients from non-patients and measure the progression of disease in each patient over time using a 3D volume review.
Proceedings ArticleDOI
Attention on Attention for Image Captioning
TL;DR: AoANet as mentioned in this paper proposes an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries and achieves state-of-the-art performance.
Proceedings ArticleDOI
Zero-Shot Learning — The Good, the Bad and the Ugly
TL;DR: A new benchmark is defined by unifying both the evaluation protocols and data splits for zero-shot learning, and a significant number of the state-of-the-art methods are compared and analyzed in depth, both in the classic zero- shot setting but also in the more realistic generalized zero-shots setting.
Book ChapterDOI
The sixth visual object tracking VOT2018 challenge results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Roman Pflugfelder,Luka Čehovin Zajc,Tomas Vojir,Goutam Bhat,Alan Lukežič,Abdelrahman Eldesokey,Gustavo Fernandez,Alvaro Garcia-Martin,Álvaro Iglesias-Arias,A. Aydin Alatan,Abel Gonzalez-Garcia,Alfredo Petrosino,Alireza Memarmoghadam,Andrea Vedaldi,Andrej Muhič,Anfeng He,Arnold W. M. Smeulders,Asanka G. Perera,Bo Li,Boyu Chen,Changick Kim,Changsheng Xu,Changzhen Xiong,Cheng Tian,Chong Luo,Chong Sun,Cong Hao,Daijin Kim,Deepak Mishra,Deming Chen,Dong Wang,Dongyoon Wee,Efstratios Gavves,Erhan Gundogdu,Erik Velasco-Salido,Fahad Shahbaz Khan,Fan Yang,Fei Zhao,Feng Li,Francesco Battistone,George De Ath,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Haibin Ling,Hamed Kiani Galoogahi,Hankyeol Lee,Haojie Li,Haojie Zhao,Heng Fan,Honggang Zhang,Horst Possegger,Houqiang Li,Huchuan Lu,Hui Zhi,Huiyun Li,Hyemin Lee,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jaime Spencer Martin,Javaan Chahl,Jin-Young Choi,Jing Li,Jinqiao Wang,Jinqing Qi,Jinyoung Sung,Joakim Johnander,João F. Henriques,Jongwon Choi,Joost van de Weijer,Jorge Rodríguez Herranz,Jorge Rodríguez Herranz,José M. Martínez,Josef Kittler,Junfei Zhuang,Junyu Gao,Klemen Grm,Lichao Zhang,Lijun Wang,Lingxiao Yang,Litu Rout,Liu Si,Luca Bertinetto,Lutao Chu,Manqiang Che,Mario Edoardo Maresca,Martin Danelljan,Ming-Hsuan Yang,Mohamed H. Abdelpakey,Mohamed Shehata,Myunggu Kang,Namhoon Lee,Ning Wang,Ondrej Miksik,Payman Moallem,Pablo Vicente-Moñivar,Pedro Senna,Peixia Li,Philip H. S. Torr,Priya Mariam Raju,Qian Ruihe,Qiang Wang,Qin Zhou,Qing Guo,Rafael Martin-Nieto,Rama Krishna Sai Subrahmanyam Gorthi,Ran Tao,Richard Bowden,Richard M. Everson,Runling Wang,Sangdoo Yun,Seokeon Choi,Sergio Vivas,Shuai Bai,Shuangping Huang,Sihang Wu,Simon Hadfield,Siwen Wang,Stuart Golodetz,Tang Ming,Tianyang Xu,Tianzhu Zhang,Tobias Fischer,Vincenzo Santopietro,Vitomir Struc,Wang Wei,Wangmeng Zuo,Wei Feng,Wei Wu,Wei Zou,Weiming Hu,Wengang Zhou,Wenjun Zeng,Xiaofan Zhang,Xiaohe Wu,Xiaojun Wu,Xinmei Tian,Yan Li,Yan Lu,Yee Wei Law,Yi Wu,Yi Wu,Yiannis Demiris,Yicai Yang,Yifan Jiao,Yuhong Li,Yuhong Li,Yunhua Zhang,Yuxuan Sun,Zheng Zhang,Zheng Zhu,Zhen-Hua Feng,Zhihui Wang,Zhiqun He +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI
Gibson Env: Real-World Perception for Embodied Agents
TL;DR: Gibson as discussed by the authors is a real-world environment for active agents to learn visual perception tasks in real-time and is based upon virtualizing real spaces, rather than artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings.
References
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Proceedings ArticleDOI
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