CVML Programming Short Course and Workshop on Deep Learning and Computer Vision 2023
This three-day short course provides an in-depth presentation of programming tools and techniques for various computer vision and deep learning problems. The target application domains are autonomous systems (e.g., real time object detection) and digital/social media analysis for Natural Disaster Management. The short course consists of three parts (A, B, C), each having lectures and programming workshops with hands-on lab exercises.
Part A will focus on Deep Learning and GPU programming. The lectures of this part provide a solid background on Deep Neural Networks (DNN) topics, notably convolutional NNs (CNNs) and deep learning for image classification. Also, Knowledge Distillation methods in DNNs will be presented. Two programming workshops will take place. The first one will be on image classification using CNNs, while the second one will be on knowledge distillation for different DNN architectures to achieve faster inference times in embedded systems.
Part B lectures will focus on deep learning algorithms for Perception on Autonomous Systems, namely on 2D object/face detection and 2D object tracking. The hands-on programming workshop will be on target detection with PyTorch and on how to use OpenCV (the most used library for computer vision) for target tracking.
Part C lectures will focus on Autonomous Systems in Natural Disaster Management (NDM). The lectures will provide a basic understanding of Real-Time Image Segmentation algorithms. The partitioners will be able to use DNNs in a hands-on programming workshop for Image Segmentation on Natural Disaster Optical Flow data (e.g., videos of floods). Moreover, methods for using Natural Language Processing in NDM will be presented. A programming workshop on exploiting text data from social media (e.g., twitter) with DNNs will take place.
The lectures and programming tools will provide programming skills for the various computer vision and deep learning problems encountered in Autonomous Systems for Natural Disaster Management, e.g., neural knowledge distillation, real-time object detection, tracking, image segmentation, NLP etc.
Lectures and programming workshops will be in English. PDF files will be available at the end of the course.
Part A (8 hours) Deep Learning for Autonomous Systems
- Deep neural networks – Convolutional NNs.
- Knowledge Distillation in Deep Neural Networks.
- Programming workshop on Deep neural networks – Convolutional NNs.
- Programming workshop on Knowledge Distillation in Deep Neural Networks.
Part B (8 hours) Autonomous Systems Perception
- Real Time Object Detection.
- 2D Object Tracking in Embedded Systems.
- Programming workshop on Real Time Object Detection.
- Programming workshop on 2D Object Tracking in Embedded Systems.
Part C (8 hours) Autnomous Systems in Natural Disaster Management
- Real-Time Image Segmentation.
- Natural Language Processing for Natural Disaster Management.
- Programming workshop on Real-Time Image Segmentation.
- Programming workshop on Natural Language Processing for Natural Disaster Management.
WHEN?
The course will take place between August 30 and September 1 2023.
WHERE?
All lectures and workshops will be delivered on-site in KEDEA buidling.
You can find additional information about the city of Thessaloniki and details on how to get to the city here.
HOW?
Each registrant will use her/his own computer for a) participating in the course and b) for running the programming exercises. A standard PC with a stable internet connection is required. The participants are also required to own a Google account for the workshops exercises.
The course will be delivered in KEDEA buidling of Aristotle University of Thessaloniki campus (see map above).
PROGRAM
| Date/time* | 30/08/2023 | 31/08/2023 | 01/09/2023 |
| Topic | Deep Learning for Autonomous Systems | Autonomous Systems Perception | Autnomous Systems in Natural Disaster Management |
| 8:00-8:30 | Registration | ||
| LECTURES | LECTURES | LECTURES | |
| 8:30-9:00 | Introduction to Autonomous systems | ||
| 9:00- 10:00 | Deep neural networks – Convolutional NNs | Real Time Object Detection | Real-Time Image Segmentation |
| 10:00-11:00 | Knowledge Distillation in Deep Neural Networks | 2D Object Tracking in Embedded Systems | Natural Language Processing for Natural Disaster Management |
| 11:00-11:30 | Coffee break | ||
| WORKSHOPS | WORKSHOPS | WORKSHOPS | |
| 11:30-13:30 | Programming workshop on Deep neural networks – Convolutional NNs | Programming workshop on Real Time Object Detection | Programming workshop on Real-Time Image Segmentation |
| 13:30-14:30 | Lunch break | ||
| 14:30-16:30 | Programming workshop on Knowledge Distillation in Deep Neural Networks | Programming workshop on 2D Object Tracking in Embedded Systems | Programming workshop on Natural Language Processing for Natural Disaster Management |
* Eastern European Summer Time (EEST, UTC+3 hours)
** This programme is indicative and may be modified without prior notice by announcing (hopefully small) changes in lectures/lecturers.
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REGISTRATION
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General Registration
Early registration (till 15/07/2023):
• Standard: 200 Euros
• Reduced registration for young professionals (up to 2 years after graduation): 100 Euros
• Unemployed or Undergraduate/MSc/PhD student*: 50 Euros
Later or on-site registration (after 15/07/2023):
• Standard: 200 Euros
• Reduced registration for young professionals (up to 2 years after graduation): 110 Euros
• Unemployed or Undergraduate/MSc/PhD student*: 60 Euro
After the completion of your payment, please fill in the form below:
A certificate of attendance will be provided by AUTH upon successful completion of the course.
Cancelation policy:
- 50% refund for cancelation up to 31/07/2023
- 0% refund afterwards
Every effort will be undertaken to run the course as planned. Due to the special COVID-19 circumstances, the organizer (AUTH) reserves right to cancel the event anytime by simple notice to the registrants (by email by announcing it in the course www page). In this case, each registrant will be reimbursed 100% for the registration fee. However, the organizer will be not held liable for any other loss incurred to the registrants.
