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Abhinav Goel
Researcher at Purdue University
Publications - 31
Citations - 249
Abhinav Goel is an academic researcher from Purdue University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 6, co-authored 17 publications receiving 112 citations. Previous affiliations of Abhinav Goel include PES University.
Papers
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Journal ArticleDOI
Low-Power Computer Vision: Status, Challenges, and Opportunities
Sergei Alyamkin,Matthew Ardi,Alexander C. Berg,Achille Brighton,Bo Chen,Yi Chen,Hsin-Pai Cheng,Zichen Fan,Chen Feng,Bo Fu,Kent Gauen,Abhinav Goel,Alexander Goncharenko,Xuyang Guo,Soonhoi Ha,Andrew Howard,Xiao Hu,Yuanjun Huang,Dong-Hyun Kang,Jaeyoun Kim,Jong-Gook Ko,Alexander Kondratyev,Jun-Hyeok Lee,Seungjae Lee,Suwoong Lee,Zichao Li,Zhiyu Liang,Juzheng Liu,Xin Liu,Yang Lu,Yung-Hsiang Lu,Deeptanshu Malik,Hong Hanh Nguyen,Eunbyung Park,Denis Repin,Liang Shen,Tao Sheng,Fei Sun,David Svitov,George K. Thiruvathukal,Baiwu Zhang,Jingchi Zhang,Xiaopeng Zhang,Shaojie Zhuo +43 more
TL;DR: The state of the art for low-power solutions to detect objects in images is examined to suggest directions for research as well as opportunities forLow-power computer vision.
Posted Content
A Survey of Methods for Low-Power Deep Learning and Computer Vision
TL;DR: This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models.
Proceedings ArticleDOI
A Survey of Methods for Low-Power Deep Learning and Computer Vision
TL;DR: In this paper, the authors survey the progress of low-power deep learning and computer vision, specifically in regards to inference, and discuss the methods for compacting and accelerating DNN models.
Journal ArticleDOI
Modular Neural Networks for Low-Power Image Classification on Embedded Devices
Abhinav Goel,Sara Aghajanzadeh,Caleb Tung,Shuo-Han Chen,George K. Thiruvathukal,Yung-Hsiang Lu +5 more
TL;DR: The Modular Neural Network Tree architecture is introduced, which uses multiple smaller DNNs (called modules) to progressively classify images into groups of categories based on a novel visual similarity metric.
Proceedings ArticleDOI
Camera Placement Meeting Restrictions of Computer Vision
Sara Aghajanzadeh,Roopasree Naidu,Shuo-Han Chen,Caleb Tung,Abhinav Goel,Yung-Hsiang Lu,George K. Thiruvathukal +6 more
TL;DR: This study proposes a camera placement method that identifies effective camera placement in arbitrary spaces and can account for different camera types as well, and makes it possible to perform object tracking via overlapping camera placement.