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Vision and Robotics Seminar

ThursdayDec 21, 201712:15
Vision and Robotics SeminarRoom 1
Speaker:Assaf ShocherTitle:“Zero-Shot” Super-Resolution using Deep Internal LearningAbstract:opens in new windowin html    pdfopens in new window

Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training data, where the acquisition of the low-resolution (LR) images from their high-resolution (HR) counterparts is predetermined (e.g., bicubic downscaling), without any distracting artifacts (e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images, however, rarely obey these restrictions, resulting in poor SR results by SotA (State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which exploits the power of Deep Learning, but does not rely on prior training. We exploit the internal recurrence of information inside a single image, and train a small image-specific CNN at test time, on examples extracted solely from the input image itself. As such, it can adapt itself to different settings per image. This allows to perform SR of real old photos, noisy images, biological data, and other images where the acquisition process is unknown or non-ideal. On such images, our method outperforms SotA CNN-based SR methods, as well as previous unsupervised SR methods. To the best of our knowledge, this is the first unsupervised CNN-based SR method.

ThursdayDec 28, 201712:15
Vision and Robotics SeminarRoom 1
Speaker:Greg ShakhnarovichTitle:TBAAbstract:opens in new windowin html    pdfopens in new window
TBA
ThursdayJan 04, 201812:15
Vision and Robotics SeminarRoom 1
Speaker:Guy Gilboa Title:TBAAbstract:opens in new windowin html    pdfopens in new window
ThursdayJan 11, 201812:15
Vision and Robotics SeminarRoom 1
Speaker:Oren SalzmanTitle:Computational Challenges and Algorithms in Planning for Robotic SystemsAbstract:opens in new windowin html    pdfopens in new window

In recent years, robots have played an active role in everyday life: medical robots assist in complex surgeries, low-cost commercial robots clean houses and fleets of robots are used to efficiently manage warehouses. A key challenge in these systems is motion planning, where we are interested in planning a collision-free path for a robot in an environment cluttered with obstacles. While the general problem has been studied for several decades now, these new applications introduce an abundance of new challenges.

In this talk I will describe some of these challenges as well as algorithms developed to address them. I will overview general challenges such as compression and graph-search algorithms in the context of motion planning. I will show why traditional Computer Science tools are ill-suited for these problems and introduce alternative algorithms that leverage the unique characteristics of robot motion planning. In addition, I will describe domains-specific challenges such as those that arise when planning for assistive robots and for humanoid robots and overview algorithms tailored for these specific domains.

ThursdayJan 18, 201812:15
Vision and Robotics SeminarRoom 1
Speaker:Sagie BenaimTitle:TBAAbstract:opens in new windowin html    pdfopens in new window
ThursdayJan 25, 201812:15
Vision and Robotics SeminarRoom 1
Speaker:Hallel Bunis and Elon Rimon ‎Title:TBAAbstract:opens in new windowin html    pdfopens in new window
ThursdayFeb 01, 201812:15
Vision and Robotics SeminarRoom 1
Speaker:Haggai MaromTitle:TBAAbstract:opens in new windowin html    pdfopens in new window