Segui
Mahmoud Famouri
Mahmoud Famouri
Senior Machine Learning Engineer
Email verificata su uwaterloo.ca
Titolo
Citata da
Citata da
Anno
YOLO nano: A highly compact you only look once convolutional neural network for object detection
A Wong, M Famouri, MJ Shafiee, F Li, B Chwyl, J Chung
arXiv preprint arXiv:1910.01271, 2019
1352019
Document image binarization using a discriminative structural classifier
E Ahmadi, Z Azimifar, M Shams, M Famouri, MJ Shafiee
Pattern recognition letters 63, 36-42, 2015
432015
Attendnets: tiny deep image recognition neural networks for the edge via visual attention condensers
A Wong, M Famouri, MJ Shafiee
arXiv preprint arXiv:2009.14385, 2020
322020
Tinyspeech: Attention condensers for deep speech recognition neural networks on edge devices
A Wong, M Famouri, M Pavlova, S Surana
arXiv preprint arXiv:2008.04245, 2020
302020
A novel motion plane-based approach to vehicle speed estimation
M Famouri, Z Azimifar, A Wong
IEEE Transactions on Intelligent Transportation Systems 20 (4), 1237-1246, 2018
292018
Fibrosis-Net: a tailored deep convolutional neural network design for prediction of pulmonary fibrosis progression from chest CT images
A Wong, J Lu, A Dorfman, P McInnis, M Famouri, D Manary, JRH Lee, ...
Frontiers in artificial intelligence 4, 764047, 2021
192021
Fast linear svm validation based on early stopping in iterative learning
M Famouri, M Taheri, Z Azimifar
International Journal of Pattern Recognition and Artificial Intelligence 29 …, 2015
192015
Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images
JRH Lee, M Pavlova, M Famouri, A Wong
BMC Medical Imaging 22 (1), 143, 2022
142022
OutlierNets: Highly compact deep autoencoder network architectures for on-device acoustic anomaly detection
S Abbasi, M Famouri, MJ Shafiee, A Wong
Sensors 21 (14), 4805, 2021
142021
DepthNet nano: A highly compact self-normalizing neural network for monocular depth estimation
L Wang, M Famouri, A Wong
arXiv preprint arXiv:2004.08008, 2020
142020
Yolo nano: A highly compact you only look once convolutional neural network for object detection. arxiv 2019
A Wong, M Famuori, MJ Shafiee, F Li, B Chwyl, J Chung
arXiv preprint arXiv:1910.01271 10, 2019
102019
Fast shape-from-template using local features
M Famouri, A Bartoli, Z Azimifar
Machine Vision and Applications, 1–21, 2017
102017
A robust probabilistic Braille recognition system
M Yousefi, M Famouri, B Nasihatkon, Z Azimifar, P Fieguth
International Journal on Document Analysis and Recognition (IJDAR) 15, 253-266, 2012
102012
AttendSeg: A tiny attention condenser neural network for semantic segmentation on the edge
X Wen, M Famouri, A Hryniowski, A Wong
arXiv preprint arXiv:2104.14623, 2021
62021
Faster attention is what you need: a fast self-attention neural network backbone architecture for the edge via double-condensing attention condensers
A Wong, MJ Shafiee, S Abbasi, S Nair, M Famouri
arXiv preprint arXiv:2208.06980, 2022
52022
Celldefectnet: A machine-designed attention condenser network for electroluminescence-based photovoltaic cell defect inspection
C Xu, M Famouri, G Bathla, S Nair, MJ Shafiee, A Wong
2022 19th Conference on Robots and Vision (CRV), 219-223, 2022
52022
COVID-net clinical ICU: Enhanced prediction of ICU admission for COVID-19 patients via explainability and trust quantification
A Chung, M Famouri, A Hryniowski, A Wong
arXiv preprint arXiv:2109.06711, 2021
52021
Tinyspeech: Attention condensers for deep speech recognition neural networks on edge devices. arXiv 2020
A Wong, M Famouri, M Pavlova, S Surana
arXiv preprint arXiv:2008.04245, 0
5
Tinydefectnet: Highly compact deep neural network architecture for high-throughput manufacturing visual quality inspection
MJ Shafiee, M Famouri, G Bathla, F Li, A Wong
arXiv preprint arXiv:2111.14319, 2021
42021
Quick sift (QSIFT), an approach to reduce SIFT computational cost
Z Fazel, M Famouri, A Nazemi, Z Azimifar
2017 Artificial Intelligence and Signal Processing Conference (AISP), 337-340, 2017
22017
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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