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 vision foundation models, 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 Location

The course will happen at the Amsterdam Science Park conference centre at Science Park 125, close to CWI.

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 January 13, 2025: Foundations

TimeRoom TopicLecturer
0900-0930FoyerWelcome with coffee and tea
0930-1030TuringzaalWhat foundation models cannot perceiveCees Snoek
1030-1100Break
1100-1200TuringzaalGrounding Foundation Models in Reality: Physics- & Causality-informed World ModelsEfstratios Gavves
1200-1330NewtonzaalLunch break (included)
1330-1600EulerzaalLab session 

Tuesday January 14, 2025: Machine learning for computer vision

TimeRoom TopicLecturer
0900-0930FoyerWelcome with coffee and tea
0930-1030TuringzaalHyperbolic deep learningPascal Mettes
1030-1100Break
1100-1200TuringzaalLearning of time and dynamicsEfstratios Gavves 
1200-1330NewtonzaalLunch break (included)
1330-1600EulerzaalLab session 

Wednesday January 15, 2025: 3D  vision by learning

TimeRoom TopicLecturer
0900-0930FoyerWelcome with coffee and tea
0930-1030Turingzaal3D representation learningMartin Oswald
1030-1100Break
1100-1200Turingzaal3D human-centric perception and synthesisDimitris Tzionas 
1200-1330NewtonzaalLunch break (included)
1330-1600EulerzaalLab session 

Thursday January 16, 2025: Computer video by learning

TimeRoom TopicLecturer
0900-0930FoyerWelcome with coffee and tea
0930-1010TuringzaalLearning to Generalize in Video Space and TimeCees Snoek
1010-1020Short break
1020-1100TuringzaalData and Evaluation in Video Understanding Hazel Doughty
1100-1130Break
1130-1215TuringzaalObject-centric representations for real-world videosAndrii Zadaianchuk
1215-1330NewtonzaalLunch break (included)
1330-1600EulerzaalLab session 

Friday January 17, 2025: Invited tutorial by Yuki Asano

TimeRoom TopicLecturer
0900-0930FoyerWelcome with coffee and tea
0930-1030TuringzaalBetter Foundation Models: Self-supervised Learning for GeneralisationYuki Asano
1030-1100Break
1100-1200TuringzaalBetter Foundation Models: Self-supervised Learning for GeneralisationYuki Asano
1200-1330NewtonzaalLunch break (included)


Invited tutorial

  • Yuki Asano

    is head of the Fundamental AI (FunAI) Lab and full Professor at the University of Technology Nuremberg. Prior to this, he led the QUVA lab at the University of Amsterdam, where he closely collaborated with Qualcomm AI Research. His PhD was at the Visual Geometry Group (VGG) at the University of Oxford.

Lecturers

  • 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 two public-private AI research labs: QUVA Lab with Qualcomm and Atlas Lab with TomTom. He also leads the HAVA-Lab, an interdisciplinary PhD-programme of the UvA Data Science Centre that aligns video-AI technologies with human values and ethical principles. 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.

  • 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

  • Hazel Doughty

    is an Assistant Professor at Leiden University. Previously she was a postdoctoral researcher at the University of Amsterdam and a PhD student at the University of Bristol. Her area of interest is Video Understanding, focusing on fine-grained and detailed video understanding with weak, noisy or other forms of incomplete supervision.

  • Pascal Mettes

    is an Assistant Professor at the University of Amsterdam. He received his PhD in 2017 at the University of Amsterdam and was a visiting scientist at Columbia University in 2016. His research interests are in hyperbolic learning for computer vision.

  • Martin Oswald

    is an assistant professor at the Atlas lab of the University of Amsterdam. He was previously a the Computer Vision and Geometry Group at ETH Zurich. He obtained his PhD at Technische Universität München.

  • Dimitris Tzionas

    is an Assistant Professor for 3D Computer Vision at the University of Amsterdam. Earlier, he was a Research Scientist and postdoc at the Perceiving Systems department at MPI for Intelligent Systems. He received a PhD from the University of Bonn.

  • Andrii Zadaianchuk

    is a Postdoctoral Researcher in VIS Lab, University of Amsterdam. He obtained his PhD at Max Planck ETH Center for Learning Systems, Switzerland. Additionally, he has done several internships in Amazon AWS Lablets team.