Course Description

Over the last years, the field of Computer Vision has experienced a tremendous growth. This is evident in the ever-growing number of participants of the main Computer Vision conferences, as well as industrial and public interest in this area of research. As part of this growth, the number of paper submissions is increasing at a rapid pace. Unfortunately, the number of experienced reviewers is not increasing at the same rate. Partially, this can be linked to the fact that many senior researchers are nowadays (partially) affiliated with industry and thus do not have the time anymore to educate their PhD students about writing reviews. Another reason is the ever-increasing range of topics presented at CVPR/ECCV/ICCV, which makes it harder and harder to find experts for each submission. As a result, the quality of the reviews seems to be decreasing, leading to more random decisions and thus frustration in the community. It is left to us as a research community to make an effort to actively work on increasing the quality of reviews by educating our increasing pool of reviewers.

The goal of this tutorial is to provide information to our community on how to write better reviews, thus making a first step towards increasing the quality of the review process. Naturally, there are multiple ways to write a good review and there is no single template for this process. To account for this, this tutorial plans to address the question "How to write a good review?" from multiple perspectives:

Researchers who recently received outstanding reviewer awards at CVPR / ECCV / ICCV will provide their unique perspective on how they approach reviews, what they do consider good practice in reviewing a paper, how they provide feedback to authors in a clear and concise way, and how they ensure that their feedback is useful for the authors.
(Former) Program and Area Chairs will provide their perspective on what kind of reviews are most useful to make a decision on a paper.
Senior researcher will explain how they approach paper writing, and how its writing can affect the impact of a research paper.

We believe that by providing multiple perspectives on the topic, attendees will be able to better understand the "do's and don'ts of reviewing”, and as a consequence, also improve their paper writing skills, hopefully making the reviewer's work, in turn, easier.

Invited Speakers

Vittorio Ferrari

Vittorio Ferrari is a Senior Staff Research Scientist at Google, where he leads a research group on visual learning. He received his PhD from ETH Zurich in 2004, then was a post-doc at INRIA Grenoble (2006-2007) and at the University of Oxford (2007-2008). Between 2008 and 2012 he was an Assistant Professor at ETH Zurich, funded by a Swiss National Science Foundation Professorship grant. In 2012-2018 he was faculty at the University of Edinburgh, where he became a Full Professor in 2016 (now he is a Honorary Professor). In 2012 he received the prestigious ERC Starting Grant, and the best paper award from the European Conference in Computer Vision. He is the author of over 110 technical publications. He regularly serves as an Area Chair for the major computer vision conferences, he was a Program Chair for ECCV 2018 and is a General Chair for ECCV 2020. He is an Associate Editor of IEEE Pattern Analysis and Machine Intelligence. His current research interests are in learning visual models with minimal human supervision, human-machine collaboration, and semantic segmentation.

Andrew Fitzgibbon

Andrew Fitzgibbon leads the "All Data AI" (ADA) research group at Microsoft in Cambridge, UK. He is best known for his work on 3D vision, having been a core contributor to the Emmy-award-winning 3D camera tracker \boujou\, to body tracking for Kinect for Xbox 360, and for the articulated hand-tracking interface to Microsoft's HoloLens. His research interests are broad, spanning computer vision, machine learning, programming languages, computer graphics and occasionally a little neuroscience. He has published numerous highly-cited papers, and received many awards for his work, including ten "best paper" prizes at various venues, the Silver medal of the Royal Academy of Engineering, and the BCS Roger Needham award. He is a fellow of the Royal Academy of Engineering, the British Computer Society, and the International Association for Pattern Recognition, and is a Distinguished Fellow of the British Machine Vision Association. Before joining Microsoft in 2005, he was a Royal Society University Research Fellow at Oxford University, having previously studied at Edinburgh University, Heriot-Watt, and University College, Cork.

Bill Freeman

William T. Freeman is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science (EECS) at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) there. He was the Associate Department Head of EECS from 2011 - 2014. His current research interests include mid-level vision and computational photography. Previous research topics include steerable filters and pyramids, orientation histograms, the generic viewpoint assumption, color constancy, computer vision for computer games, motion magnification, and belief propagation in networks with loops. He received outstanding paper awards at computer vision or machine learning conferences in 1997, 2006, 2009, 2012 and 2019, and test-of-time awards for papers from 1990, 1995 and 2005. He shared 1/347th of the 2020 Breakthrough Prize in Physics for a consulting role with the Event Horizon Telescope collaboration, which reconstructed the first image of a black hole. He is a Fellow of IEEE, ACM, and AAAI. In 2019, he received the PAMI Distinguished Researcher Award. He is active in the program or organizing committees of computer vision, graphics, and machine learning conferences. He was the program co-chair for ICCV 2005, and for CVPR 2013. He holds over 30 patents.

Michael Goesele

Michael Goesele is a research scientist at Facebook Reality Labs in Redmond, WA, USA. Before joining FRL in October 2018, he was a professor in the Department of Computer Science at TU Darmstadt, a postdoctoral research associate at the University of Washington and a research associate at the Max Planck Institute for Computer Science. His research interests span the fields of computer vision, computer graphics and high performance computing with a focus on reconstruction and rendering of geometry and appearance. He holds a Diploma in Computer Science from Ulm University and a Ph.D. from Saarland University.

Fatma Güney

Fatma Güney is an Assistant Professor at the Dept. of Computer Engineering at Koç University in Istanbul. She was previously a postdoc at VGG, at the University of Oxford, working with Andrea Vedaldi and Andrew Zisserman; and a Ph.D. student at the MPI for Intelligent Systems, working with Andreas Geiger. She is interested in 3D computer vision and representation learning from video sequences. Currently working on modeling object-object relations in a video, e.g. multi-object tracking, video object detection, and background motion modellng. She previously worked on action recognition, optical flow estimation, depth estimation, and multi-view 3D reconstruction. Fatma received Outstanding Reviewer awards at CVPR 2018 and CVPR 2019 (specially mentioned as one reviewer who contributed "at least four reviews noted as excellent by area chairs").

Jordi Pont-Tuset

Jordi Pont-Tuset is a research scientist at Google Research Zurich since 2018. Previously, he was a post-doctoral researcher at ETHZ, Switzerland (2015-2017). He worked at Disney Research Zurich (2014). He received a degree in Mathematics in 2008, a degree in Electrical Engineering in 2008, and a Ph.D with honors in 2014; all from the Universitat Politècnica de Catalunya, BarcelonaTech (UPC). Jordi received Outstanding Reviewer awards at ICCV 2019 and CVPR 2019 (specially mentioned as one reviewer who contributed "at least four reviews noted as excellent by area chairs") and received recognition as an emergency reviewer for ICCV 2017, ICCV 2019, and CVPR 2019.

Greg Mori

Dr. Greg Mori was born in Vancouver and grew up in Richmond, BC. He received the Ph.D. degree in Computer Science from the University of California, Berkeley in 2004. He received an Hon. B.Sc. in Computer Science and Mathematics with High Distinction from the University of Toronto in 1999. He spent one year (1997-1998) as an intern at Advanced Telecommunications Research (ATR) in Kyoto, Japan. After graduating from Berkeley, he returned home to Vancouver and is currently a Professor in the School of Computing Science at Simon Fraser University.

He was a Visiting Scientist at Google in Mountain View, California in 2014-2015. He served as Director of the School of Computing Science from 2015-2018. He is now Research Director for RBC's Borealis AI Vancouver lab.

Dr. Mori conducts research in computer vision and machine learning, and teaches classes in data structures and programming, artificial intelligence, computer vision, and machine learning. Dr. Mori received the Canadian Image Processing and Pattern Recognition Society (CIPPRS) Award for Research Excellence and Service in 2008. Dr. Mori received NSERC Discovery Accelerator Supplement awards in 2008 and 2015. He received the ICCV Helmholtz Prize in 2017. He served on the editorial boards of IJCV and T-PAMI, the top journals in computer vision, and on the organizing committees for NeurIPS, CVPR, ICCV, and ECCV, the top conferences in computer vision and machine learning. He will be a Program Chair for CVPR 2020 and a General Chair for CVPR 2023. He is privileged to have worked with many excellent students while at SFU.

Konrad Schindler

Konrad Schindler received a Diplomingenieur (M.tech) degree in photogrammetry from Vienna University of Technology, Austria in 1999, and a PhD from Graz University of Technology, Austria, in 2003. He has worked as a photogrammetric engineer in the private industry, and held researcher positions in the Computer Graphics and Vision Department of Graz University of Technology, the Digital Perception Lab of Monash University, and the Computer Vision Lab of ETH Zurich. He became assistant professor of Image Understanding at TU Darmstadt in 2009, and since 2010 has been a tenured professor of Photogrammetry and Remote Sensing at ETH Zurich. His research interests lie in the field of computer vision, photogrammetry, and remote sensing, with a focus on image understanding and 3D reconstruction. He currently serves as head of the Institute of Geodesy and Photogrammetry, and as associate editor for the ISPRS Journal of Photogrammetry and Remote Sensing, and for the Image and Vision Computing Journal. Konrad was an AC for ECCV 2018 and received Outstanding Reviewer awards at CVPR 2018, ICCV 2019, and CVPR 2019.

Rick Szeliski

Rick Szeliski is a Research Scientist in the Computational Photography group at Facebook. He is also an Aliate Professor at the University of Washington, and is a member of the NAE and a Fellow of the ACM and IEEE. He received his Ph.D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988 and joined Facebook as founding Director of the Computational Photography group in 2015. Prior to Facebook, he worked at Microsoft Research for twenty years, the Cambridge Research Lab of Digital Equipment Corporation for six years, and several other industrial research labs. He has published over 150 research papers on computer vision, computer graphics, neural nets, and numerical analysis, as well as the books Computer Vision: Algorithms and Applications and Bayesian Modeling of Uncertainty in Low-Level Vision. He was a Program Committee Chair for CVPR 2013 and ICCV 2003, served as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and on the Editorial Board of the International Journal of Computer Vision, and as Founding Editor of Foundations and Trends in Computer Graphics and Vision. Dr. Szeliski has done pioneering research in the elds of Bayesian methods for computer vision, image-based modeling, image-based rendering, and computational photography, which lie at the intersection of computer vision and computer graphics. His research on Photo Tourism, Photosynth, and Hyperlapse are exciting examples of the promise of large-scale image and video-based rendering. He won the Helmholtz Price at ICCV 2019.