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Dynamic scene understanding using deep neural networks
Mapping Intimacies
◽
10.3990/1.9789036552233
◽
2021
◽
Author(s):
Ye Lyu
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Scene Understanding
◽
Dynamic Scene
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Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems
2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)
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10.1109/icps49255.2021.9468126
◽
2021
◽
Author(s):
Stavros Nousias
◽
Erion Vasilis Pikoulis
◽
Christos Mavrokefalidis
◽
Aris S. Lalos
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Scene Understanding
◽
Cyber Physical Systems
◽
Physical Systems
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Scene Understanding Using Deep Neural Networks—Objects, Actions, and Events: A Review
Advances in Intelligent Systems and Computing - International Conference on Innovative Computing and Communications
◽
10.1007/978-981-15-1286-5_19
◽
2020
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pp. 223-231
Author(s):
Ranjini Surendran
◽
D. Jude Hemanth
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Scene Understanding
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Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL
RoboCup 2018: Robot World Cup XXII - Lecture Notes in Computer Science
◽
10.1007/978-3-030-27544-0_8
◽
2019
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pp. 96-108
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Cited By ~ 3
Author(s):
Marton Szemenyei
◽
Vladimir Estivill-Castro
Keyword(s):
Neural Networks
◽
Real Time
◽
Deep Neural Networks
◽
Scene Understanding
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Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT
10.32470/ccn.2018.1046-0
◽
2018
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Cited By ~ 1
Author(s):
Alex Hernández-García
◽
Johannes Mehrer
◽
Nikolaus Kriegeskorte
◽
Peter König
◽
Tim C. Kietzmann
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Data Augmentation
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Representation of adversarial images in deep neural networks and the human brain
10.32470/ccn.2018.1066-0
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2018
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Author(s):
Chi Zhang
◽
Xiaohan Duan
◽
Ruyuan Zhang
◽
Li Tong
Keyword(s):
Neural Networks
◽
Human Brain
◽
Deep Neural Networks
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Study on Intelligent Security Camera Systems for the Automated Detection of Nighttime Snatching Incidents using Deep Neural Networks
IEEJ Transactions on Industry Applications
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10.1541/ieejias.136.727
◽
2016
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Vol 136
(10)
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pp. 727-734
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Cited By ~ 2
Author(s):
Itaru Nagayama
◽
Akira Miyahara
◽
Koichi Shimabukuro
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Automated Detection
◽
Camera Systems
◽
Intelligent Security
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Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters
2020 28th European Signal Processing Conference (EUSIPCO)
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10.23919/eusipco47968.2020.9287739
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2021
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Author(s):
Niccolo Nicodemo
◽
Gaurav Naithani
◽
Konstantinos Drossos
◽
Tuomas Virtanen
◽
Roberto Saletti
Keyword(s):
Neural Networks
◽
Deep Neural Networks
◽
Field Programmable Gate Arrays
◽
Memory Requirement
◽
Gate Arrays
◽
Field Programmable
◽
Programmable Gate Arrays
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The Use of Deep Neural Networks to Predict Post-liver Transplant Mortality
10.26226/morressier.58eb975cd462b80290b4f9e3
◽
2017
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Author(s):
Brent Ershoff
Keyword(s):
Neural Networks
◽
Liver Transplant
◽
Deep Neural Networks
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Financial Market Prediction and Improving the Performance Based on Large-scale Exogenous Variables and Deep Neural Networks
Korean Institute of Smart Media
◽
10.30693/smj.2020.9.4.26
◽
2020
◽
Vol 9
(4)
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pp. 26-35
Author(s):
Sung Gil Cheon
◽
Ju Hong Lee
◽
Bum Ghi Choi
◽
Jae Won Song
Keyword(s):
Neural Networks
◽
Financial Market
◽
Large Scale
◽
Deep Neural Networks
◽
Exogenous Variables
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Faculty Opinions recommendation of Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature
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10.3410/f.726413891.793534418
◽
2017
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Author(s):
Odelia Schwartz
Keyword(s):
Neural Networks
◽
Object Recognition
◽
Deep Neural Networks
◽
Visual Object
◽
Visual Object Recognition
◽
Cortical Dynamics
◽
Spatio Temporal
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