论文标题
部分视觉语义嵌入：与敏感的部分学习的时尚智能系统
Partial Visual-Semantic Embedding: Fashion Intelligence System with Sensitive Part-by-Part Learning
论文作者
论文摘要
在这项研究中，我们提出了一项基于视觉语义嵌入（VSE）模型的技术，称为时尚情报系统，以量化时尚特有的抽象和复杂表达方式，例如“休闲，”''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''，并支持对时尚用户的理解。但是，现有的VSE模型不支持图像由头发，上衣，裤子，裙子和鞋子等多个部分组成的情况。我们提出了部分VSE，该VSE可以为时尚坐标的每个部分提供敏感的学习。提出的模型部分学习了嵌入式表示。这有助于保留各种现有的实践功能，并启用图像 - 重新检查任务，其中仅对指定零件的指定零件和图像重新排序的任务进行更改。传统模型不可能。基于定性和定量评估实验，我们表明所提出的模型优于常规模型，而无需增加计算复杂性。
In this study, we propose a technology called the Fashion Intelligence System based on the visual-semantic embedding (VSE) model to quantify abstract and complex expressions unique to fashion, such as ''casual,'' ''adult-casual,'' and ''office-casual,'' and to support users' understanding of fashion. However, the existing VSE model does not support the situations in which the image is composed of multiple parts such as hair, tops, pants, skirts, and shoes. We propose partial VSE, which enables sensitive learning for each part of the fashion coordinates. The proposed model partially learns embedded representations. This helps retain the various existing practical functionalities and enables image-retrieval tasks in which changes are made only to the specified parts and image reordering tasks that focus on the specified parts. This was not possible with conventional models. Based on both the qualitative and quantitative evaluation experiments, we show that the proposed model is superior to conventional models without increasing the computational complexity.
