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M
Zhao, Bo, Wu, Xiao, Cheng, Zhi-Qi, Liu, Hao, Jie, Zequn, Feng, Jiashi.  2018.  Multi-View Image Generation from a Single-View. Proceedings of the 26th ACM International Conference on Multimedia. :383-391.

How to generate multi-view images with realistic-looking appearance from only a single view input is a challenging problem. In this paper, we attack this problem by proposing a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs). It generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produces coarse images of different views. Conditioned on the generated coarse images, it then performs adversarial learning to fill details consistent with the input and generate the fine images. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that the generated images with the proposed VariGANs are more plausible than those generated by existing approaches, which provide more consistent global appearance as well as richer and sharper details.

E
Li, Gaochao, Jin, Xin, Wang, Zhonghua, Chen, Xunxun, Wu, Xiao.  2018.  Expert Recommendation Based on Collaborative Filtering in Subject Research. Proceedings of the 2018 International Conference on Information Science and System. :291–298.

This article implements a method for expert recommendation based on collaborative filtering. The recommendation model extracts potential evaluation experts from historical data, figures out the relevance between past subjects and current subjects, obtains the evaluation experience index and personal ability index of experts, calculates the relevance of research direction between experts and subjects and finally recommends the most proper experts.