Course Summary
This graduate course is especially meant for Ph.D. students who have basic familiarity with computer vision, image processing, and machine learning and want to upsurge their knowledge and machinery to the state-of-the-art, with direct utility in their own research.
The topic of attention is the challenge of computer vision by learning. We address the theoretical foundations of computer vision in conjunction with machine learning and present algorithms that achieve state-of-the-art performance while maintaining efficient execution with minimal supervision. This year we explain and emphasize on computer vision by deep learning, including challenges like 3D object detction, fine-grained recognition, geometric deep learning, self-supervised representation learning and video understanding . We give an overview of the latest developments and future trends in the field on the basis of several recent challenges, and we indicate how to obtain improvements in the near future.
Course Registration
Course registration is handled by the ASCI research school, via this form. Note that the number of seats for this course is limited.
Lab requirements: bring your own device
For the lab, you are expected to bring your own device, either a laptop with a good GPU or a laptop that can connect to a workstation with a good GPU. In case you cannot connect to a GPU, you should make a CoLAB Google Account and make sure you can run a GPU powered notebook (You can turn the GPU on by the following steps: Edit->Notebook settings->Hardware accelerator->GPU). The lab assignments are detailed on a separate page.
Course Schedule
Monday May 9, 2022: Fundamentals
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | Casa | Welcome with coffee and tea | |
0930-1010 | Casa | Introduction and vision by learning basics | Cees Snoek |
1010-1020 | Short break | ||
1020-1100 | Casa | Vision in the deep learning era | Efstratios Gavves |
1100-1130 | Break | ||
1130-1215 | Casa | Deep learning beyond classification | Efstratios Gavves |
1215-1330 | Lunch break (included) | ||
1330-1700 | Casa | Lab session - day 1: MLP / CNN |
Tuesday May 10, 2022: Computer vision by deep learning
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | Casa | Welcome with coffee and tea | |
0930-1010 | Casa | Transformers | Cees Snoek |
1010-1020 | Short break | ||
1020-1100 | Casa | Learning from little data | Subhransu Maji |
1100-1130 | Break | ||
1130-1215 | Casa | 3D representation learning | Martin Oswald |
1215-1330 | Lunch break (included) | ||
1330-1700 | Casa | Lab session - day 2: CNN / Transformer |
Wednesday May 11, 2022: Machine learning for computer vision
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | Casa | Welcome with coffee and tea | |
0930-1030 | Casa | Group equivariant deep learning | Erik Bekkers |
1030-1100 | Break | ||
1100-1200 | Casa | Learning of time and dynamics | Efstratios Gavves |
1200-1330 | Lunch break (included) | ||
1330-1700 | Casa | Lab session - day 3: Geometric deep learning |
Thursday May 12, 2022: Computer video by learning
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | Casa | Welcome with coffee and tea | |
0930-1010 | Casa | Self-supervised learning | Yuki Asano |
1010-1020 | Short break | ||
1020-1100 | Casa | Action understanding in video | Hazel Doughty |
1100-1130 | Break | ||
1130-1215 | Casa | Beyond spatial classification | Cees Snoek |
1215-1330 | Lunch break (included) | ||
1330-1700 | Casa | Lab session - day 4: Self-supervised learning | |
1700-1800 | Casa | Closing borrel with drinks and snacks |
Friday May 13, 2022: Invited tutorial by Serge Belongie
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | Startup Village | Welcome with coffee and tea | |
0930-1045 | Startup Village | Fine-grained visual analysis | Serge Belongie |
1045-1115 | Break | ||
1115-1215 | Startup Village | Representation learning for narratives in social media | Serge Belongie |
1215-1230 | Closing |
Invited tutorial
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Serge Belongie
is a professor of Computer Science at the University of Copenhagen, where he also serves as the head of the Danish Pioneer Centre for Artificial Intelligence. Previously, he was the Andrew H. and Ann R. Tisch Professor of Computer Science at Cornell Tech, where he also served as Associate Dean. He has also been a member of the Visiting Faculty program at Google.
Lecturers
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Cees Snoek
is full professor in computer science at the University of Amsterdam, where he heads the Video & Image Sense Lab. He is also a director of three public-private AI research labs: QUVA Lab with Qualcomm, Atlas Lab with TomTom and AIM Lab with the Inception Institute of Artificial Intelligence. He was a visiting scientist at Carnegie Mellon University, Pittsburgh and the University of California, Berkeley. His research interest is video and image understanding by computer vision and machine learning.
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Efstratios Gavves
is an Associate Professor with the University of Amsterdam in the Netherlands. He received his Ph.D. in 2014 at the University of Amsterdam. He was a post-doctoral researcher at the KU Leuven from 2014 - 2015. He has authored several papers in major computer vision and machine learning conferences and journals. He is a recipient of the ERC Career Starting Grant 2020 and NWO VIDI grant 2020 to research on the Computational Learning of Temporality for spatiotemporal sequences.
Guest Lecturers
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Yuki M. Asano
is an assistant professor for computer vision and machine learning at the QUVA lab at the University of Amsterdam. He did his PhD at the Visual Geometry Group at the University of Oxford. He is interested in computer vision, self-supervised learning and multi-modal learning.
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Erik Bekkers
is an assistant professor in Geometric Deep Learning at the University of Amsterdam. Before this he did a post-doc in applied differential geometry at the Technical University Eindhoven. Erik is a recipient of a MICCAI Young Scientist Award 2018, Philips Impact Award and a personal VENI research grant.
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Hazel Doughty
is a Postdoctoral researcher at the University of Amsterdam. She received her PhD in 2020 at the University of Bristol and was a visiting researcher at INRIA Willow (Paris) in 2019. Her area of interest is Video Understanding.
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Subhransu Maji
an Associate Professor at the University of Massachusetts, Amherst and the co-director of the Computer Vision Lab. Prior to this he spent three years as a Research Assistant Professor at TTI Chicago.