The recent surge in machine learning and in particular deep learning using neural network has revolutionized many fields including speech processing, data mining and medicine. Arguably one of the greatest impacts of this revolution is in computer vision. Since the success of AlexNet at the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) where the deep neural network solution outperformed, by a significant margin, arguably much more advanced classical computer vision systems, deep neural networks can now be found everywhere in visual processing. This revolution has created enormous economic interest (Facebook, Google, …). In electrical and computer engineers are expected to apply these neural networks techniques from machine learning in various computer vision application including in IoT and robotics. This topics course is to address computer vision in a principled manner. While it necessarily includes machine learning background, it specifically looks at neural networks and their applications to standard problems in computer vision. It will also contrast the deep-learning based approaches to classical computer vision approaches and how classical approaches inform the design of these deep-learning based solutions.
Introduction to learning-based computer vision; statistical learning background; image processing and filtering primer; convolutional neural networks (CNNs), network layers, computer vision data sets and competitions; computer vision problems, in particular, image classification, detection and recognition, semantic segmentation, image generation, multi-view problems and tracking.
The course material will be covered in synchronous and asynchronous on-line lectures including program demonstrations. Additional resources in form of textbooks and on-line references are listed below. The course will be using group work and interactive student feedback using Virtual Campus (Brightspace) and Microsoft Teams. Students are encouraged to apply their knowledge through three programming assignments in Jupyter notebooks using Scikit-Learn, Keras and Tensorflow. Participation in the course requires approriate access to resources. The active participation of students is encouraged through discussions, the group video presentation and the individual student project presentations.
(Available on-line: http://web.stanford.edu/~hastie/ElemStatLearn/
(Pre-print available on-line: http://szeliski.org/Book)
(Electronic version available for download from library http://biblio.uottawa.ca/en)
(Available on-line:http://www.deeplearningbook.org)
(Jupyter notebooks can be found at https://github.com/ageron/handson-ml3 ).
(Available from http://cs231n.github.io)
(Free online book from http://neuralnetworksanddeeplearning.com/)
Course notes will be made available through Virtual Campus.
ImageNet competition, commercial applications, brief historical overview, machine learning landscape, data handling, visualizing data, organizing the data: training, testing and validation
Linear regression review, linear least squares, regularization, logistic regression
Multi-layer perceptron, feed forward networks, activation functions, loss function, and training by back propagation. Gradient descent and stochastic gradient descent
Correlation, convolution and linear filters
Convolutional, pooling and fully-connected layers, visualizing CNNs
Initialization, transfer learning, data augmentation, regularization, dropout, mini-batch normalization, data sets and competitions
ImageNet competitions, metrics, regions with CNNs (R-CNN), fully-convolutional networks (FCNNs), U-Net, one-stage detectors, You Only Look Once (YOLO) detector, SSD: Single-Shot MultiBox Detector, instance segmentation
Benchmarks and metrics, hourglass networks, cascade design, attention layers and multi-task networks. Applications as face detector, people detector and pedestrian detector.
Vanilla transformer: attention, positional encoding and normalization. Image transformer, DETR, ViT, Swin transformer.
Problem description for stereo and optical flow, geometric constraints and brief overview of classical methods, metrics.
Network designs for pixelwise classification and regression. Supervised and unsupervised training. Loss functions, occlusion handling.
Tracking by detection, discriminative and generative models, part-based trackers, discriminative correlation filters, siamese networks, short-term and long-term tracking, multi-object tracking, on-line tracking and real-time tracking, video object segmentation.
Image generation, image-to-image translation and style transfer. Variational autoencoders (VAE) and generative adversarial networks (GAN).
Student evaluation will be based on assignments and a project.
The maximum is 100 marks*) with the following breakdown:
3 Programming assignments (using Tensorflow, Jupyter notebooks)
|
20 marks |
| Lab session at DEBI | 10 marks |
| Project including oral presentation
Marked in oral progress meetings (see Virtual Campus for more detail). Project must be done in groups 5. A video presentation is required. |
40 marks |
| Final Exam
Closed book. |
30 marks |
All components of the course (i.e. assignments, projects, etc.) must be fulfilled otherwise students may receive an INC as a final mark (equivalent to an F). This also holds for a student who is taking the course for the second time.
Any form of plagiarism or fraud including on an assignment or the project will be reported.
For any plagiarism or fraud the university regulation on academic integrity and misconduct applies. Two websites detail the rules surrounding academic fraud and the rules regarding the use of AI. Please familiarize yourself with them.