Deep Learning for Computer Vision

The research field of computer vision addresses the problems related to acquiring, processing, analyzing, and understanding visual information such as images, videos, and 3D point clouds. One of the core problems in computer vision is visual learning and recognition, i.e., learning the representations (of objects, faces, rooms, actions, etc.) that are later used to classify unknown instances that appear in new images. This problem has been tackled since the beginning of the computer vision. However, no previously proposed method has increased the performance beyond the current state of the art like deep learning approaches in recent years. Convolutional neural networks and related deep learning approaches have proven to be a very efficient way of finding the representations and building a classifier in a unified framework that yields excellent results in various computer vision tasks. The main goal of this course is to introduce students to the field of deep learning, with a special emphasis on its application in computer vision. The students will be acquainted with the main principles of computer vision and machine learning, relating them to neural network methods and showing them how to train and use neural networks, emphasizing Convolutional Neural Networks. It will be shown how these approaches can be used for object classification, localization, and detection, as well as for other tasks in computer vision and beyond.

Restrictions/Prerequisites: Solid knowledge of computer vision and machine learning, programming skills