Tutorials
Please register to the tutorials via
https://www.eventbrite.com/e/nais-2022-tutorials-tickets-336399539077.
You can contact Cise Midoglu (cise@simula.no) for questions and comments.
Goal! A practical guide to soccer video understanding
Anthony Cioppa (ULiège), Silvio Giancola (KAUST), Adrien Deliège (ULiège),
Marc Van
Droogenbroeck (ULiège), and Bernard Ghanem (KAUST)
Date: Wednesday, June 1 @
13:00-16:00Duration: 3 hours
The SoccerNet dataset released in 2018 marked the start of large-scale soccer analysis in
academia,
gathering a growing research community which now expands to the industry. Broadcast soccer video
understanding is an attractive topic for graduate students with many potential applications,
like
highlights composition and statistics generation. Besides, it encompasses natural yet
challenging
tasks for computer vision professionals, such as action spotting, camera calibration, player
re-identification and tracking. It also comes with specific difficulties to handle fast-paced
actions, players of similar appearance and replays through various camera views. All these
aspects
make soccer a rich yet often overlooked playground for research.
This tutorial focuses on the practical side of building soccer video understanding pipelines:
which
data is available, how to annotate it, how to use it, which useful tasks can be defined,
tackled,
and assessed, and which challenges keep the community and industries busy. Demos with Python
code
will be presented step-by-step to cover a large panel of soccer-related tasks. The instructors
and
presenters of the tutorial are experienced scientists from academia and industry that lead the
soccer research community and develop cutting-edge technologies for sports broadcasts.
This tutorial is tailored for computer vision master students and their professors seeking
computer
vision classes or thesis projects, for PhD candidates focusing on spatio-temporal aspects of
video
analysis, for researchers and industrials willing to apply AI techniques within sports
broadcasts,
and for any soccer enthusiast. The download information of the SoccerNet dataset indicates that
all
those types of profiles regularly use the dataset. The tutorial assumes basic knowledge of
Python
and neural networks. Upon completion of the tutorial, attendees will have at hand various
pipelines
to tackle tasks such as action spotting, player tracking, player re-identification, camera
calibration, that they can use not only in soccer-related projects but also transfer to their
own
research. All the material produced within the tutorial will be made available online.
Search Algorithms in AI with Python
Rashmi Gupta (UiA) and Morten Goodwin (UiA)
Date: Tuesday, May 31 @
09:00-12:00Duration: 3 hours
In artificial intelligence (AI) and computer science in general, search is a step-by-step process of
solving a problem following a particular search space. When it comes to problem-solving, AI is
highly dependent on search algorithms, such as finding the most suitable solution from a human-like
virtual assistant, finding the most convenient route from self-driving cars, or the most promising
move in a chess game. Search algorithms are the building blocks of AI evolving with this futuristic
technology and are relevant to the topics of interest in the Norwegian AI Symposium (NAIS 2022). We
strongly believe learning search algorithms with python could provide additional value to the
Norwegian AI community and early-stage researchers.
This tutorial covers classical uninformed (blind) searching algorithms in AI such as breadth-first
search, uniform cost search, depth-first search, iterative deepening depth-first search,
bidirectional search, and informed (heuristic) searching algorithms in AI such as best-first search
and A* search. We consider finding the solutions for real-world problems (i.e., specific to each
search algorithm) by implementing these search algorithms in python, which we believe will provide
better technical support to target attendees. We propose to spend this three-hour tutorial. In the
first hour for the coverage of uninformed searching algorithms, organize a minor in-class assignment
of 15 minutes in duration. We then cover informed searching algorithms in one hour, followed by a
small assignment on informed search algorithms of 15 minutes. We wrap up the tutorial with the
source material and other valuable information or discussion.
This tutorial targets Bachelor’s/Master's level computer science students with an
introductory/intermediate level of any programming language knowledge (presenter will use python in
tutorial).
The past, present, and future of XAI
Kristoffer Wickstrøm (UiT)
Date: Wednesday, June 1 @
14:00-15:00Duration: 45
minutes
Deep learning is the main component in contemporary artificial intelligence algorithms, which have
seen major improvements in fields such as computer vision and natural language processing. However,
deep learning lacks explainability, which limits its usability in fields such as finance and
medicine where trustworthiness is of high importance. Explainable artificial intelligence (XAI) aims
at making deep learning more transparent and reliable, and has made great improvements over the last
couple of years. Being familiar with XAI methodology can be advantageous for both machine learning
researchers and more applied researchers, and a tutorial on XAI would therefore be of great use for
the Norwegian AI community.
The tutorial will start with an introduction to the field of XAI and a presentation of a selection
of explainability algorithms. Then, we will discuss some common pitfalls and challenges with current
XAI, before moving on to discussing what makes an explanation good and where the field will go in
the future.
The turtorial targets machine learning researchers with intermediate knowledge of XAI.