3rd June 2020
11. Deep Learning. Convolutional Neural Networks 17:00-17:45 EEST
12. Deep Object Detection 17:45-18:30 EEST
LECTURE DESCRIPTION
LECTURE 11: Deep Learning. Convolutional Neural Networks
Wednesday 3rd June 2020, 17:00-17:45 EEST
Summary: Introduction to deep learning, focusing on convolutional neural networks (CNNs).From multilayer perceptrons to deep architectures. Fully connected layers. Convolutional layers. Tensors and mathematical CNN formulations. Pooling. Training convolutional NNs. Initialization. Batch Normalization, Data augmentation. Regularization. Dropout. AlexNet, ZFNet, ResNet, SqueezeNet, Inception, GoogleLeNet, Network-In-Network architectures.Lightweight deep learning. Deployment on embedded systems. Performance metrics.
Sample lecture material
– sample slides DOWNLOAD
Related Literature:
- LeCun Y, Bengio Y, Hinton G. ‘Deep learning’ Nature, 2015 May;521(7553):436-44.
- I.Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016
Lecture 12: Deep Object Detection
Wednesday 3rd June2020, 17:45-18:30 EEST
Summary: An overview is provided on target detection using deep neural networks. Detection as classification and regression task, Modern architectures for target detection: RCNN, Faster RCNN, R-FCN, YOLO v1/2/3/4, SSD Lightweight detector architectures. Object detection performance metrics. Evaluation and benchmarking. Deployment in embedded platforms.
Recently, Convolutional Neural Networks (CNNs) have been used for the task of object detection with great results. However, using such models on drones for real-time face detection is prohibited by the hardware constraints that drones impose. Various architectures and settings are examined to facilitate the use of CNN-based object detectors on a drone with limited computational capabilities.
Sample lecture material
– sample slides DOWNLOAD
Related Literature
- P. Nousi, E. Patsiouras, A. Tefas, I. Pitas, Convolutional Neural Networks for Visual Information Analysis with Limited Computing Resources, 2018 IEEE International Conference on Image Processing (ICIP), Athens, Greece, October 7-10, 2018
- Bochkovskiy, CY Wang and HY M. Liao. “YOLOv4: Optimal Speed and Accuracy of Object Detection”. arXiv, 2020
- Ren, Shaoqing, et al. “Faster R-CNN: Towards real-time object detection with region proposal networks.” Advances in Neural Information Processing Systems. 2015.
- Liu, Wei, et al. “SSD: Single Shot Multibox Detector.” European Conference on Computer Vision. 2016
LECTURER
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University.
His current interests are in the areas of machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 860 papers, contributed in 44 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 69 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 41 such projects.
He has 31000+ citations to his work and h-index 83+ (Google Scholar)
Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/ and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/.
Lecturing record of Prof. I. Pitas: He was Visiting/Adjunct/Honorary Professor/Researcher and lectured at several Universities: University of Toronto (Canada), University of British Columbia (Canada), EPFL (Switzerland), Chinese Academy of Sciences (China), University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, Henan University (China). He delivered 90 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He run 17 short courses and tutorials on Autonomous Systems, Computer Vision and Machine Learning, most of them in the past 3 years in many countries, e.g., USA, UK, Italy, Finland, Greece, Australia, N. Zealand, Korea, Taiwan, Sri Lanka, Bhutan.
Relevant links:
http://https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=en