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November 2017 The magazine of the algorithm community November 2017 Can we see around corners? - Katie Bouman Upcoming Events: AI EXPO BEST OF ICCV: 33 pages!!! Presentations, People and Technology: Women in Computer Vision: Raquel Urtasun, Georgia Gkioxari and … Laura Leal-Taixé Vicky Kalogeiton Spotlight News Project Management: Iffy If's by Ron Soferman We Tried for You: Use Recurrent Neural Networks with Attention Image Processing: Bones Segmentation from CT Scans Review of Research Paper by Berkeley: Unpaired Image-to-Image Translation… A publication by 2 Read This Month Computer Vision News Tutorial - FAIR and Spotlight News We Tried for You Georgia Gkioxari RNN with Attention 37 04 50 Research of the month Project TorontoCity by Berkeley Bones Segmentation Raquel Urtasun - UofT 08 38 Can we see aroundcorners? 55 Katie L. Bouman - MIT Project Management by Ron Soferman Upcoming Events AI Expo NA 10 48 56 04 BEST OF ICCV Daily 2017 37 Spotlight News Tutorial, with Georgia Gkioxari From elsewhere on the Web Presentations: 38 Review of Research Paper TorontoCity, with Raquel Urtasun Unpaired Image-to-Image Turning Corners Into Cameras, K. Bouman Translation using Cycle-Consistent Focal Track, with Qi Guo Adversarial Networks - by A. Spanier SceneNet RGB-D, with John McCormac 48 Project Management Detect to Track, with C. Feichtenhofer Iffy If's - by Ron Soferman Weakly-Supervised Learning, Julia Peyre We Tried for You Active Learning for Human Pose Est., B. Liu 50 RNN with Attention - by A. Spanier Women in Science: Project in Computer Vision Laura Leal-Taixé, 55 Bones Segmentation from CT Scans Vicky Kalogeiton 56 Computer Vision Events Interview: AI Expo and Nov-Jan events Cristian Canton Ferrer - Facebook Welcome 3 Computer Vision News Dear reader, This October issue of Computer Vision News is obviously dedicated to the exceptional success of ICCV2017: expecting about 1,800 participants (following the 1,400 attendees of the previous edition), organizers were surprised by about 3,200 registrations, one more proof of the spectacular growth of the computer vision community. RSIP Vision was obviously in the first row at ICCV: first by publishing the very first ICCV Daily; second, by publishing today a very insightful BEST OF ICCV, which you can start reading in the next page. You will find in it short and long testimonies of the most impressive lectures and works, from Jitendra Malik to Michael Black, from Raquel Urtasun to Georgia Gkioxari and more. Among the many inspiring presentations, we dedicate our cover of Computer Vision News November to Katie Bouman of MIT, who taught us how we can see around corners: Editor: intriguing and exciting! Let me thank ICCV (in Ralph Anzarouth particular Marcello Pelillo and Nicu Sebe) for partnering with us and letting us cover the Engineering Editor: conference with our brand new ICCV Daily Assaf Spanier publication. Publisher: In addition to ICCV, you will read in this RSIP Vision November issue of Computer Vision News many more articles, including: our own Contact us reviews of research and tools; the full preview of AI Expo in the Silicon Valley; the list of Give us feedback upcoming computer vision events; the Free subscription Spotlight News; and more… Read previous magazines Enjoy the reading! Copyright: RSIP Vision All rights reserved Ralph Anzarouth Unauthorized reproduction Marketing Manager, RSIP Vision is strictly forbidden. Editor, Computer Vision News 4 Opening Talk Computer Vision News General Chair Marcello Pelillo during the Opening Talk of ICCV2017. He was kind enough to dedicate one of his slides to the ICCV Daily, the new publication originated by the partnership between ICCV and Computer Vision News, the magazine of the algorithm community published by RSIP Vision. This was the first ICCV Daily ever, made possible by the conference chairs’ resolute will to tool up ICCV with the same daily magazine as CVPR, ECCV, MICCAI and CARS. Once again, this concept born from a rib of Computer Vision News proved extremely popular and successful. Here is probably the right place to thank all those who helped this first ICCV Daily project become a reality: Nicu Sebe, Octavia Camps, Rita Cucchiara and the professional event staff of theoffice.it (in particular Federica and Laura) SpeakersSpeakers 5 Computer Vision News Jitendra Malik at ICCV2017, talking at Beyond Supervised Learning workshop. Quoting Donald Knuth, Jitendra said: “If it works once, it’s a hack; if it works more than once it’s a technique!” Michael Black at ICCV2017, answering questions at the PoseTrack Challenge: Human Pose Estimation and Tracking in the Wild workshop. Michael is now Distinguished Scholar at Amazon. 6 Tutorial - Georgia Gkioxari Computer Vision News Instance-level Visual Recognition Georgia Gkioxari is a postdoctoral researcher at FAIR. She received her PhD from UC Berkeley, where she was advised by Jitendra Malik. She is the organizer of tutorial Instance- level Visual Recognition at ICCV2017. Georgia, you organised a tutorial on Sunday. Can you tell us about it? The tutorial was on instance-level visual recognition, which means that we tried to describe and cover the topics regarding scene understanding and object understanding. Whose initiative was it? I think it was a FAIR initiative from their researchers at Facebook AI Research. However, I was the one leading it, We actually covered a wide variety of organising it, reaching out to speakers, topics. Ross Girshick presented a making sure that everybody has their generalised description of R-CNN for talks ready and they are all in sync. object detection. Later on, Kaiming He covered Mask R-CNN and tried to show Why is it important for us to get into a different perspective of this work. I this subject? covered human-object interactions, Object recognition and scene which is a field that is growing right understanding have been very popular now and is of great interest to the subjects and very popular fields of community. Jifeng Dai covered video study in computer vision over a span of understanding. Last but not least, 30 years or more. It is very important Justin Johnson tried to go beyond to always keep up-to-date with the those topics and cover visual recent and best methods out there, relationships as well as reasoning. and always try to make it clear to the audience, even if they are not “I would like to see video specialists in this field, to understand understanding take off” what is going on. What recent findings in this area were Computer Vision News already people able to learn about at the reviewed outstanding work by Georgia tutorial? Gkioxari and FAIR. Read it here Tutorial - Georgia Gkioxari 7 Computer Vision News Computer Vision News It seems that Facebook is getting the lead in this kind of subject. Is that right to say and if so, why is that? I think that is a fair statement. I think that FAIR has… [we laugh at the unintended pun] Yes, FAIR with big letters… Exactly! I think that FAIR has some of the best researchers in the field of object recognition and scene understanding, with people such as Ross, Kaiming, Piotr Dollár. As well as others, such as Laurens Van der Maaten, Rob Fergus, and so on. It is definitely a group of very good scientists that are experts in this, but this is not just what they can do. They can research and make progress in a lot of fields that are related to object recognition, but not only. That is a good question and it is a hard one, because I think the fields that we “It would be great if we have not made a breakthrough in are plenty. I would identify two. I would could find more effective like to see video understanding take ways of learning through off. I would like us to be able to understand videos better. Not just data, and use less and less through better datasets, but also labelled data to achieve through more efficient and effective methods. The other direction that I the same performance” think we have not seen a lot of What findings would you like to see in progress in is unsupervised learning. the next couple of years? Currently, we are very good at learning and training systems with millions and millions of labelled data. However, it would be great if we could find more effective ways of learning through data, and use less and less labelled data to achieve the same performance. “I covered human-object interactions, which is a field that is growing right now and is of great interest to the community” 8 TorontoCity Computer Vision News TorontoCity: Seeing the World With a Million Eyes Min Bai and Shenlong Wang are both PhD students at the University of Toronto, supervised by Professor Raquel Urtasun. All are part of the Uber Advanced Technologies Group (ATG) in Toronto, Canada (managed by Raquel). We spoke to Min, Shenlong and Raquel ahead of their poster today, which is co-authored with Gellért Máttyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, and Sanja Fidler. From left: Min, Shenlong and Raquel Their work is about a super large-scale allows people to train machine learning dataset, captured from different models that didn’t exist before. perspectives and with all kinds of Shenlong says that a motivation for this different sensors. From top-down view work is that they would like the and ground level, and with LIDAR and community to realise the importance RGB camera. The key is to annotate the of mapping. He says that mapping is a ground truth with existing high- very important problem and there is definition maps. not such a good benchmark to Human annotation is expensive.
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