Description

Anticipation is one of the main and most powerful neuro-cognitive mechanisms of our brain. We heavily rely on it for each of our daily activity: from preparing breakfast to driving a car we continuously figure out what will happen next to better interact with the environment, based on our knowledge of the world around us. However, anticipating future actions still represent a big challenge for machines. One of the main reasons is the difficult in acquiring, representing and leveraging the knowledge our environment for future predictions.
This project aims at addressing this challenge in the specific context of future action prediction by developing a deep learning model based on transformer networks that have proved to be effective in modeling long-range dependencies.
The student is expected to have excellent programming skills, familiarity with deep learning frameworks (preferably PyTorch) and a good mathematical background. Working during seven months under this project will allow the student to learn about the most cutting-edge techniques in deep learning and computer vision, as well as to evaluate them in realistic scenarios. Upon reaching the project goals, a scientific publication is expected to be submitted to a major conference or journal.