With the boom of the fashion market and people’s daily needs for beauty, clothing matching has gained increased research attention. In a sense, tackling this problem lies in modeling the human notions of the compatibility between fashion items, i.e., Fashion Compatibility Modeling (FCM), which 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, these studies work like a black box and cannot provide appropriate explanations, which are indeed of importance for gaining users’ trust and improving their experience. In fact, fashion experts usually explain the compatibility evaluation through the matching patterns between fashion attributes (e.g., a silk tank top cannot go with a knit dress). Inspired by this, we devise an attribute-wise explainable FCM solution, named ExFCM , which can simultaneously generate the item-level compatibility evaluation for input fashion items and the attribute-level explanations for the evaluation result. In particular, ExFCM consists of two key components: attribute-wise representation learning and attribute interaction modeling. The former works on learning the region-aware attribute representation for each item with the threshold global average pooling. Besides, the latter is responsible for compiling the attribute-level matching signals into the overall compatibility evaluation adaptively with the attentive interaction mechanism. Note that ExFCM is trained without any attribute-level compatibility annotations, which facilitates its practical applications. Extensive experiments on two real-world datasets validate that ExFCM can generate more accurate compatibility evaluations than the existing methods, together with reasonable explanations.

中文翻译：

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属性可解释的时尚兼容性建模

随着时尚市场的繁荣和人们对美的日常需求，服装搭配越来越受到研究关注。从某种意义上说，解决这个问题的关键在于对时尚物品之间的兼容性概念进行建模，即时尚兼容性建模（FCM），它在包括服装推荐和穿衣助手在内的大量商业应用中发挥着重要作用。多媒体处理的最新进展在准确的兼容性评估中显示出显着的有效性。然而，这些研究工作就像一个黑匣子，无法提供适当的解释，这对于获得用户的信任和改善他们的体验确实很重要。事实上，时尚专家通常通过时尚属性之间的匹配模式（例如，丝绸背心不能搭配针织裙子）。受此启发，我们设计了一种属性可解释的 FCM 解决方案，命名为ExFCM，可以同时生成输入时尚单品的单品级兼容性评价和评价结果的属性级解释。特别是，ExFCM 由两个关键组件组成：属性表示学习和属性交互建模。前者致力于使用阈值全局平均池来学习每个项目的区域感知属性表示。此外，后者负责通过注意力交互机制将属性级匹配信号自适应地编译到整体兼容性评估中。请注意，ExFCM 是在没有任何属性级兼容性注释的情况下进行训练的，这有助于其实际应用。