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首页> 外文期刊>Multimedia, IEEE Transactions on >Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data
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Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data

机译：在数据集上使用端到端深度学习方法挖掘时尚服装成分

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摘要

Composing fashion outfits involves deep under-standing of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., jewelry, bag, pants, dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates based on the appearances and metadata. We propose to leverage outfit popularity on fashion-oriented websites to supervise the scoring component. The scoring component is a multimodal multiinstance deep learning system that evaluates instance aesthetics and set compatibility simultaneously. In order to train and evaluate the proposed composition system, we have collected a large-scale fashion outfit dataset with 195K outfits and 368K fashion items from Polyvore. Although the fashion outfit scoring and composition is rather challenging, we have achieved an AUC of 85% for the scoring component, and an accuracy of 77% for a constrained composition task.
机译：组成时尚服装需要深刻理解时尚标准，同时要结合创造力来选择多种时尚物品（例如，珠宝，包，裤子，衣服）。在时尚网站中，流行的或高品质的服装通常是由时尚专家设计的，然后是大量的受众。在本文中，我们提出了一种机器学习系统来自动组成服装。提议的自动构图系统的核心是根据外观和元数据对候选时装进行评分。我们建议在面向时尚的网站上利用服装的人气来监督评分部分。评分组件是一种多模式多实例深度学习系统，可评估实例美学并同时设置兼容性。为了训练和评估建议的构图系统，我们从Polyvore收集了19.5万件服装和36.8万件时尚商品的大规模服装数据集。尽管时尚服装的评分和构图颇具挑战性，但我们对评分组件的AUC达到了85％，对于受限的构图任务则达到了77％的准确性。

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