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

Elastic p-12C scattering by using a cluster effective field theory

The elastic p-12C scattering at low energies is studied by using a cluster effective field theory (EFT), where the low-lying resonance states (s1/2, p3/2, d5/2) of 13N are treated as pertinent degrees of freedom. The low-energy constants of the Lagrangian are expressed in terms of the Coulomb-modified effective range parameters, which are determined to reproduce the experimental data for the differential cross-sections. The resulting theoretical predictions agree very well with the experimental data. The resulting theory is shown to give us almost identical phase shifts as obtained from the R-matrix approach. The role of the ground state of 13N below the threshold and the next-to-leading order in the EFT power counting are also discussed.Comment: 17 pages, 6 figure