Starts on

Ends on

Lecturer

François Bremond,

Content and organization

This course studies Computer Vision (CV) algorithms together with their visual representations learnt through Deep Learning (DL) techniques. The studied algorithms are intended to solve traditional CV tasks, including classification, object detection and tracking, retrieval, face detection, image/video generation, emotion and action recognition and are illustrated through a panel of applications, such as video retrieval from the web, visual-surveillance, autonomous driving, merchandising, assisted living and robotics. The course discusses state-of-the-art methods from low-level description to high-level representation, and their dependence on the related CV tasks. The focus of the course is on recent, state of the art methods and large-scale applications. Cutting-edge topics will be studied, such as Convolutional Neural Networks, Recurrent Neural Networks and Generative Adversarial Networks. You will learn also to build projects in PyTorch/TensorFlow using CoLab.

Course Type

ai-phd Course

Host Institution
Université Côte d'Azur

Other short courses

11. 05. 2022 Go

Geometric learning

05. 04. 2022 Go

Computer Graphics

04. 04. 2022 Go

Bayesian Learning

02. 04. 2022 Go

Computer Graphics

31. 03. 2022 Go

Web of Data

28. 03. 2022 Go

Machine Learning

27. 03. 2022 Go

Machine Learning

21. 03. 2022 Go

Untitled

02. 03. 2022 Go

Player Modeling

28. 02. 2022 Go

Player Modeling

21. 02. 2022 Go

Affective Computing

21. 02. 2022 Go

Machine Listening

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Self-Driving Cars

21. 02. 2022 Go

Deep Learning

21. 02. 2022 Go

Deep Learning 2

17. 02. 2022 Go