Our technologies are able to analyze customer behaviorsprovide personalized automatic services. We take two directions: (1) Establish a customer behavior predictionrecommendation system by analyzing consumption records, exploring behavior features,strengthening the link between marketing strategiesbehavior analysis (2) Collaborative EDM subjects generation by analyzing the relationship between customers’ click recordsconsumption for understanding the relationship between customer intentionsfinancial products,for generating personalized marketing strategies.

We propose a semantic-guided framework (FashionOn+) that generates image-based virtual try-on results with arbitrary poses. FashionOn+ contains three stages: (I) conducts the semantic segmentation to have the prior knowledge of body parts for rendering the corresponding texture in stage (II). (III) refines two salient regions, i.e., faceclothes, to generate high-quality results. With the novel architecture, we win first place in the Multi-pose Virtual Try-on Challenge in CVPR, 2020. Further, we tackle the low-resolution limitation (256x192)achieve high-resolution results (640x480).