Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Mission design for a ballistic slow flyby Comet Encke 1980

Preliminary mission analyses for a proposed 1980 slow flyby (7-9 km/s) of comet Encke are presented. Among the topics covered are science objectives, Encke's physical activity and ephemeris accuracy, trajectory and launch-window analysis, terminal guidance, and spacecraft concepts. The nominal mission plan calls for a near-perihelion intercept with two spacecraft launched on a single launch vehicle. Both spacecraft will arrive at the same time, one passing within 500 km from Encke's nucleus on its sunward side, the other cutting through the tail region. By applying a small propulsive correction about three weeks after the encounter, it is possible to retarget both spacecraft for a second Encke intercept in 1984. The potential science return from the ballistic slow flyby is compared with other proposed mission modes for the 1980 Encke flyby mission, including the widely advocated slow flyby using solar-electric propulsion. It is shown that the ballistic slow flyby is superior in every respect