Course Summary
This graduate course is especially meant for Ph.D. students who have basic familiarity with computer vision, image processing, and pattern recognition 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 image classification by convolutional neural networks, object tracking by Siamese networks, action recognition with attention LSTMs, and event recognition by video embeddings. We give an overview of the latest developments and future trends in the field on the basis of several recent challenges, including the ImageNet and TRECVID benchmarks, the leading competitions for visual search engines based on computer vision by learning, and we indicate how to obtain improvements in the near future.
Course Schedule
Wednesday February 22, 2017: Fundamentals
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | C3.163 | Welcome with coffee and tea | |
0930-1010 | C3.163 | Introduction, Observables | Arnold Smeulders |
1010-1020 | Short break | ||
1020-1100 | C3.163 | Invariance, Codebooks | Arnold Smeulders |
1100-1130 | Break in common room - C3.239 | ||
1130-1215 | C3.163 | Encoders, Kernels | Cees Snoek |
1215-1330 | Lunch break | ||
1330-1700 | B1.24 A/B/C | Lab session - day 1 |
Thursday February 23, 2017: Computer vision by deep learning
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | C3.163 | Welcome with coffee and tea | |
0930-1010 | C3.163 | Vision in the Deep Learning Era I | Efstratios Gavves |
1010-1020 | Short break | ||
1020-1100 | C3.163 | Vision in the Deep Learning Era II | Efstratios Gavves |
1100-1130 | Break in common room - C3.239 | ||
1130-1215 | C3.163 | Action recognition by learning | Cees Snoek |
1215-1330 | Lunch break | ||
1330-1700 | B1.24 B/C/D | Lab session - day 2 |
Friday February 24, 2017: Structure, visualizationa and tracking
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | C3.163 | Welcome with coffee and tea | |
0930-1010 | C3.163 | Bringing Structure to Visual Deep Learning | Efstratios Gavves |
1010-1020 | Short break | ||
1020-1100 | C3.163 | Understanding Deep Networks Visually | Efstratios Gavves |
1100-1130 | Break in common room - C3.239 | ||
1130-1215 | C3.163 | Object tracking by learning | Arnold Smeulders |
1215-1330 | Lunch break | ||
1330-1700 | B1.24 A/B/C | Lab session - day 3 |
Monday February 27, 2017: Large-scale computer vision by learning
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | C3.163 | Welcome with coffee and tea | |
0930-1010 | C3.163 | Benchmarking | Cees Snoek |
1010-1020 | Short break | ||
1020-1100 | C3.163 | Weakly-supervised computer vision | Cees Snoek |
1100-1130 | Break in common room - C3.239 | ||
1130-1215 | C3.163 | Event recognition by learning | Amirhossein Habibian |
1215-1330 | Lunch break | ||
1330-1700 | B1.24 A/B/C | Lab session - day 4 |
Tuesday February 28, 2017: Invited tutorial by Laurens van der Maaten
Time | Room | Topic | Lecturer |
---|---|---|---|
0900-0930 | CWI - Newtonzaal | Welcome with coffee and tea | |
0930-1045 | CWI - Z009 Eulerzaal | Understanding and Improving Convolutional Networks | Laurens van der Maaten |
1045-1115 | Break - CWI Newtonzaal | ||
1115-1215 | CWI - Z009 Eulerzaal | From Visual Recognition to Visual Reasoning | Laurens van der Maaten |
1215-1330 | Lunch break | ||
1330-1600 | B1.24 A/B/C | Lab session - day 5 | Challenge Data |
1600 | Common room - C3.239 | Borrel |

Invited tutorial
-
Laurens van der Maaten
is a Research Scientist at Facebook AI Research in New York, working on machine learning and computer vision. Before, he worked as an Assistant Professor at Delft University of Technology, as a post-doctoral researcher at UC San Diego, and as a Ph.D. student at Tilburg University. He is interested in a variety of topics in machine learning and computer vision.
Lecturers
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Cees Snoek
is currently a director of the QUVA Lab, the joint research lab of Qualcomm and the University of Amsterdam on deep learning and computer vision. He is also an Associate Professor at the University of Amsterdam and Principal Engineer/Manager at Qualcomm Research Netherlands. He was a visiting scientist at Carnegie Mellon University, Pittsburgh and the University of California, Berkeley. His research interest is video and image search by computer vision and machine learning.
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Efstratios Gavves
is an Assistant 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 multimedia conferences and journals. His research interests include statistical and deep learning with applications on computer vision.
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Arnold Smeulders
is professor in visual information analysis at the University of Amsterdam. He has an interest in computer vision, content-based image retrieval and the picture-language question. Currently, he is with the University of Amsterdam, scientific director of the large public-private COMMIT research program in the Netherlands, and member of the Academia Europeana. He has graduated some 50 PhD-students. He has co-founded Euvision Technologies, an UvA-spinoff for image search engine technologies.
Guest Lecturer
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Amirhossein Habibian
received a B.Sc. in computer engineering (2008) and a M.Sc. in artificial intelligence (2011), both from University of Tehran, Iran. He received a Ph.D. at the University of Amsterdam in 2016. Currently, he is a senior engineer at Qualcomm Research in Amsterdam working on deep learning and computer vision. He received the Best Paper Award of ACM Multimedia 2014. His research interests include multimedia retrieval, computer vision and machine learning.