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The school also provides daily registration for those who are interested in taking up certain topics on interest. Certificates will be provided for all participants. Also stand a chance to win prizes for the best hands-on implementations.
With the rise of the Industrial Revolution 4.0 (IR 4.0), the need for computer vision in many applications has become indispensable. In Malaysia, industries as well as research agencies and universities, have been actively involved in applying and carrying out research in this field. A key component in successful computer vision applications is the ability of the computer algorithms in making accurate decisions.
Traditionally, machine learning approaches have achieved good performance. Nevertheless, with the advent of deep learning in the past decade, researchers have increasingly focused on taking advantage of the benefits it provides. Thus, due to its higher performance as well as its adaptability, deep learning has become very popular in computer vision applications. In this Summer School, we are proposing topics that cover both the traditional machine learning (ML) and deep learning (DL) approaches, so that researchers will benefit from the strengths of both, whilst also gaining a historical understanding for the need and importance of the transition from ML to DL in modern computer vision. These important topics and their applications in computer vision will be delivered by prominent national and international speakers. Due to the Covid-19 situation, this Summer School will be conducted in a hybrid fashion, catering for both those who prefer face-to-face participation as well as those attending virtually. All lectures and hands-on practicals will be conducted via live online sessions,
while poster sessions will be held at the conference room.
The 5-day hybrid program will cover all aspects of ML and DL, and their applications in computer vision. To gain better understanding as well as make the school more exciting and interactive, hands-on sessions will be conducted during the afternoon time slots. For ML, the school will start with an introduction before covering more advanced topics such object recognition and classification. Similarly, for DL, the school will initially cover the fundamental aspect of DL before proceeding with more advanced topics such as deep convolutional neural network, transfer and deep reinforcement learning, and deep learning architectures. Besides the regular lecture sessions, there will also be online live discussions or forums between the participants and speakers to discuss particular topics of interest in computer vision. There will also be poster sessions for participants to showcase their current work. At the end of the school, participants will be exposed to the fundamental and advanced knowledge of DL and big data, and their applications in computer vision. The expected outcome of this Summer School would be that participants would be able to create better solutions and explore greater perspectives in their domains of interest within this growing field of research.
General Chair: Syed Abd Rahman Syed Abu Bakar (Universiti Teknologi Malaysia, PhD)
Technical Program: Mohammad Faizal Ahmad Fauzi (Multimedia University, PhD)
Wong Kok Sheik (Monash University Malaysia, PhD)
Secretariat: Nor’aini Abdul Jalil (Wavesmiles, PhD)
Haidawati Mohamad Nasir (Universiti Kuala Lumpur, PhD)
Local Arrangement & Siti Armiza Mohd Aris (Universiti Teknologi Malaysia, PhD)
Recording of Sessions: Norliza Mohd Noor (Universiti Teknologi Malaysia, PhD)
Finance: Hezerul Abdul Karim (Multimedia University, PhD)
Website and Publicity: Mohd Norzali Hj Mohd (University Tun Hussein Onn Malaysia, PhD)
Poster/Forum: Usman Ullah Sheikh (Universiti Teknologi Malaysia, PhD)
Sponsorship: Rajasvaran Logeswaran (Asia Pacific University, PhD)
Over the 5 days, we propose 6 lecture/tutorial sessions covering various topics of ML and DL applications in computer vision, 4 hands-on sessions and 4 other sessions of forums and discussions with relevant industries. Posters will be displayed during the daily lunch breaks, with participants taking turns to discuss
The list of topics to be covered during the School is as summarized below:
1. Introduction to Machine Learning – Classical methods
2. Object Recognition/Classification using Classical Methods
3. Fundamental Concepts in Deep Learning
4. Variations and Advantages of Deep Learning Frameworks
5. Object Recognition/Classification using Deep Learning
6. Transfer Learning and Reinforcement Learning
7. Deep Learning for Video Processing
8. Deep Learning Future and the Way Forward
Non-technical Topics (To Select)
1. Engineers and Researchers – How to form Collaborations?
2. Industry perspectives on the need for computer vision.
3. Poster sessions
4. Forum session
5. IEEE and SPS Membership Drive
6. IEEE Senior Member Elevation Drive
7. Banquet Dinner
The final list of lecturers depends on the availability of the speakers. However, given below are the tentative list of speakers:
University of Athens
National Taiwan University of Science and Technology
Nanyang Technology University