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Abstarct

        Fashion compatibility modeling (FCM) which aims to evaluate the compatibility of fashion items, e.g., the t-shirt and trousers, plays an important role in a wide bunch of commercial applications including clothing recommendation and dressing assistant. Recent advances in multimedia processing have shown remarkable effectiveness in accurate compatibility evaluation. However, the existing methods typically lack of explanations for the evaluation, which are of importance for gaining users' trust and improving the user experience. Inspired by the fact that fashion experts explain a compatibility evaluation through matching patterns across fashion attributes (e.g., a silk tank top cannot go with a knit dress), we propose to explore the explainable FCM by leveraging comprehensible attribute semantics. As the main contribution, we devise an explainable solution, named ExFCM for FCM, which simultaneously generates the compatibility evaluation for input fashion items and explanations for the evaluation result. In particular, we first design a new neural network operator to learn the attribute-wise representation for each input fashion item. Additionally, we develop an explainable scheme to infer attribute-level matching signals between fashion items. Furthermore, the matching signals are dynamically aggregated into an overall evaluation. Note that ExFCM is trained without any attribute-level compatibility annotations. Extensive experiments on a real-world dataset verify that ExFCM generates evaluations more accurately than several state-of-the-art methods, together with reasonable explanations. Codes will be released once acceptance.

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Abstract: Welcome

Contributions

  1. We propose an explainable method (ExFCM) for fashion compatibility modeling, which generates attribute-wise explanations for compatibility evaluation.

  2. The proposed model is capable of inferring attribute-level matching signals between fashion items without any attribute-level compatibility annotations.

  3. Extensive experiments on a real-world dataset validate that ExFCM generates evaluations more accurately than several state-of-the-art methods, together with reasonable explanations.

Abstract: About

Data & Code

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Data

FashionVC comprises 20,726 outfits with 14,871 tops and 13,663 bottoms, composed by the fashion experts.

Code

Environment: Python 3.5 Tensorflow 1.4

Abstract: 檔案

Copyright (C) 2019  Shandong University


This program is licensed under the GNU General Public License 3.0 (https://www.gnu.org/licenses/gpl-3.0.html). Any derivative work obtained under this license must be licensed under the GNU General Public License as published by the Free Software Foundation, either Version 3 of the License, or (at your option) any later version, if this derivative work is distributed to a third party.


The copyright for the program is owned by Shandong University. For commercial projects that require the ability to distribute the code of this program as part of a program that cannot be distributed under the GNU General Public License, please contact joeyangbuer@gmail.com to purchase a commercial license.

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