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

Large area deep ultraviolet light of Al0.47_{0.47}Ga0.53_{0.53}N/Al0.56_{0.56}Ga0.44_{0.44}N multi quantum well with carbon nanotube electron beam pumping

Large area deep ultraviolet (DUV) light is generated by carbon nanotube (CNT) cold cathode electron beam (C-beam) irradiation on Al$_{0.47}$Ga$_{0.53}$N/Al$_{0.56}$Ga$_{0.44}$N multi quantum wells (MQWs) anode. We developed areal electron beam (EB) with CNT cold cathode emitters. The CNT emitters on silicon wafer were deposited with an area of 188 mm$^2$ , and these were vertically aligned and had conical structures. We optimized the C-beam irradiation conditions to effectively excite AlGaN MQWs. When AlGaN MQWs were excited using an anode voltage of 3 kV and an anode current of 0.8 mA, DUV with a wavelength of 278.7 nm was generated in a large area of 303 mm$^2$ . This DUV area is more than 11 times larger than the light emitting area of conventional EB pumped light sources and UV-LEDs