A novel garment transfer method supervised by distilled knowledge of virtual try-on model.

Garment transfer can wear the garment of the model image onto the personal image. As garment transfer leverages wild and cheap garment input, it has attracted tremendous attention in the community and has a huge commercial potential. Since the ground truth of garment transfer is almost unavailable in reality, previous studies have treated garment transfer as either pose transfer or garment-pose disentanglement, and trained garment transfer in self-supervised learning, However, these implementation methods do not cover garment transfer intentions completely and face the robustness issue in the testing phase. Notably, virtual try-on technology has exhibited superior performance using self-supervised learning, we propose to supervise the garment transfer training via knowledge distillation from virtual try-on. Specifically, the overall pipeline is first to infer a garment transfer parsing, and to use it to guide downstream warping and inpainting tasks. The transfer parsing reasoning model learns the response and feature knowledge from the try-on parsing reasoning model and absorbs the hard knowledge from the ground truth. The progressive flow warping model learns the content knowledge from virtual try-on for a reasonable and precise garment warping. To enhance transfer realism, we propose an arm regrowth task to infer exposed skin. Experiments demonstrate that our method has state-of-the-art performance in transferring garments between persons compared with other virtual try-on and garment transfer methods.

A novel neighbor selection scheme based on dynamic evaluation towards recommender systems.

Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.