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Mohsen Fayyaz

PhD Candidate, University of Bonn, Computer Vision Group of Prof. Dr. Juergen Gall

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About Me

Experienced Doctoral Researcher at the university of Bonn with a demonstrated history of working in the research industry. Skilled in Computer Vision and Deep Learning. Strong research professional with a Master’s Degree focused in Artificial Intelligence from MUT.

Experience

University of Bonn

Doctoral Researcher

Doctoral researcher at the University of Bonn, faculty of Computer Science III under supervision of Prof. Dr. J. Gall.

Sensifai

Deep Learning Researcher

Designing and Developing Deep Neural Networks Architectures for Computer Vision Tasks.

Iran University of Science and Technology

Graduate Student Researcher

Machine learning and Deep learning researcher in IUST HPC lab with the supervision of Prof. Dr. M. Fathy.

Education

University of Bonn

November 2017 - Now

PhD Candidate, Computer Science

Supervisor: Prof. Dr. J. Gall

MUT

September 2014 - September 2016

Master of Science in Computer Engineering - Artificial Intelligence

Supervisors: Prof. Dr. Mahmood Fathy, Dr. Mojtaba Hosseini
Advisor: Dr. Mohammad Sabokrou
Thesis: Activity Recognition in Video based on Convolutional Neural Networks
GPA: 4.00/4.00
Ranked first with highest GPA among all Computer Engineering students (AI) since 2014

Semnan University

September 2010 - September 2014

Bachelor of Science in Computer Software Engineering

Supervisor: Dr. K. Kiani
Thesis: Designing and Implementing a Cloud-based Accounting System
Ranked First among all of the university computer engineering students since the year 2010

Publications

Deep-cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes

Feb 2017 - IEEE Transactions on Image Processing
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubic patch-based method, characterized by a cascade of classifiers, which makes use of an advanced feature learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of “many” normal cubic patches.

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STFCN - Spatio-Temporal Fully Convolutional Neural Network for Semantic Segmentation of Street Scenes

Sep 2016 - ACCV
This paper presents a novel method to involve both spatial and temporal features for semantic video segmentation. Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task.

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A novel approach for Finger Vein verification based on self-taught learning

Nov 2015 - 9th Iranian Conference on Machine Vision and Image Processing (MVIP)
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders.

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Skills

  • PyTorch
  • Torch
  • Tensorflow
  • Caffe
  • Python
  • C
  • C#
  • C++

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