H
Hyungtae Lee
Researcher at Booz Allen Hamilton
Publications - 50
Citations - 2472
Hyungtae Lee is an academic researcher from Booz Allen Hamilton. The author has contributed to research in topics: Object detection & Convolutional neural network. The author has an hindex of 13, co-authored 50 publications receiving 1728 citations. Previous affiliations of Hyungtae Lee include United States Army Research Laboratory & University of Maryland, College Park.
Papers
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Proceedings ArticleDOI
A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xioyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A new large-scale video dataset designed to assess the performance of diverseVisual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage is introduced.
Journal ArticleDOI
Going Deeper With Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee,Heesung Kwon +1 more
TL;DR: A novel deep convolutional neural network that is deeper and wider than other existing deep networks for hyperspectral image classification, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors.
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
AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xiaoyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A concept for automatic construction site monitoring by taking into account 4D information (3D over time), that is acquired from highly-overlapping digital aerial images, which largely supports automated methods toward full scene understanding.
Book ChapterDOI
Weakly Supervised Localization Using Deep Feature Maps
TL;DR: This paper proposes an efficient beam search based approach to detect and localize multiple objects in images and significantly outperforms the state-of-the-art in standard object localization data-sets.