Trainings on Deep Learning for
Object Detection and Recognition

Due to its success, deep learning has prompted researchers to revisit several computer vision problems. Deep learning solutions have outperformed most of the conventional techniques in most of these scenarios. However, a one-fit-all model is far from reality as every problem comes with its own challenges. To better understand the requirements of different real-world problems and to understand how different deep learning models can be employed in the relevant situation, PPRS has been conducting a series of trainings in collaboration with Deep Learning Lab (DLL), NCAI, NUST.

The first training was conducted on the topic of Deep Learning for Object Detection & Recognition from 1st-5th March 2021, in a hybrid mode. The workshop was aimed to familiarize the participants with the underlying architecture and algorithms used for Object Detection and Recognition and get hands-on experience with the training of deep learning models. The training was a huge success amongst the participants.

Dr. Muhammad Shahzad received B.E. degree in electrical engineering from the National University of Sciences and Technology, Islamabad, Pakistan, M.Sc. degree in autonomous systems (robotics) from the Bonn Rhein Sieg University of Applied Sciences, Sankt Augustin, Germany, and his PhD degree on radar remote sensing & image anaylsis at the department of Signal Processing in Earth Observation (SiPEO), Technische Universität München (TUM), Munich, Germany in 2004, 2011 and 2016 respectively. Since 2016, he has been working as an Assistant Professor at SEECS, NUST. Furthermore, he is also a Co-Principal Investigator of the recently established Deep Learning Laboratory (DLL) under the umbrella of National Center of Artificial Intelligence (NCAI), Islamabad. His research interests include application of deep learning for processing unstructured/structured 3-D point clouds, optical RGBD data, and very high-resolution radar images.

Prof. Dr. Faisal Shafait is working as Professor in the School of Electrical Engineering and Computer Science (SEECS) at the National University of Sciences and Technology (NUST), Islamabad, Pakistan. He is also an Adjunct Professor at The University of Western Australia, Perth, Australia. His research interests include machine learning and computer vision with a special emphasis on applications in document image analysis and recognition. He is the director of Deep Learning Lab (DLL) at National Center of Artificial Intelligence (NCAI), NUST, Islamabad, Pakistan. He has received the IAPR/ICDAR Young Investigator Award by the International Association of Pattern Recognition (IAPR) in 2019 and have recently been included in the list of the World’s Top 2% Scientists compiled by Stanford University.

Dr. Adnan Ul-Hasan did his PhD in computer science from the University of Kaiserslautern, Germany. He has 9+ experience in developing text recognition algorithms. During this time, he has contributed to several projects, including digitizing 15th century Latin documents, Urdu Nastaleeq script recognition in both printed as well as handwritten forms, Postal address automation, etc. His research has been published in A* ranked conferences (ICDAR, ICPR, etc.). He is a passionate researcher and loves to talk about his research interests that include machine learning, text recognition, computer vision and deep learning. He possesses an MS degree in Control Systems Engineering from PIEAS, Islamabad and BS in Electrical Engineering from UET Taxila, Pakistan.