Contextual-based Image Inpainting: Infer, Match, and Translate

We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide the propagation of local textures from the boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train. We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.Comment: ECCV 2018 camera read

First-principles Study of the RKKY Interaction and the Quadrupole Order in the Pr 1-2-20 systems PrT2Al20 (T=Ti, V)

Electronic states and quadrupole orders in the Pr 1-2-20 systems PrT2Al20 (T=Ti, V) are investigated on the basis of the first-principles calculations. The effective 196 orbital model is derived to reproduce the first-principles electronic structures of LaT2Al20 (T=Ti, V) without contribution from the Pr 4f electrons which are considered to be well localized and is employed to calculate the Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions between quadrupole and octupole moments of the Pr ions. Within the random phase approximation for the RKKY Hamiltonian, the most divergent susceptibility is found to be the quadrupole one for the wave vector Q = (0,0,0) in the case of PrTi2Al20 while that for Q = (pi/a,0,pi/a) in the case of PrV2Al20 as consistent with experimental observations in the both cases which exhibit the ferro-quadrupole (FQ) and the antiferro-quadrupole (AFQ) orders, respectively. We also discuss the ordered states using the mean-field approximation and find that, in the case of PrTi2Al20, the 1st-order phase transition to the O20 FQ order with a tiny discontinuity takes place as predicted by the Landau theory. In the case of PrV2Al20, the system exhibits two distinct O22 AFQ orders, AFQ-I and AFQ-II, and shows subsequent two phase transitions, the 2nd-order one from normal to AFQ-I and the 1st-order one from AFQ-I to AFQ-II, that may be responsible for the double transitions observed by specific heat measurements.Comment: 6 pages, 6 figure

Unsupervised Diverse Colorization via Generative Adversarial Networks

Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible

U-Pb chronology of the unique achondrite Northwest Africa 6704.

第3回極域科学シンポジウム/第35回南極隕石シンポジウム 11月29日（木） 国立国語研究所 2階講