Face-top coordination, which exists in most clothes-fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and tradeoffs between general preferences and individual preferences. We present a Deep-Based Self-Refined (DBSR) system to simulate face-top coordination based on intuition evaluation. To this end, we first establish a well-coordinated face-top (WCFT) dataset from fashion databases and communities. Then, we use a jointly trained CNN Deep Canonical Correlation Analysis (DCCA) method to bridge the semantic face-top gap based on the WCFT dataset to deal with general preferences. Subsequently, an irrelevance-based Optimum-path Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.

中文翻译：

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Deep-based Self-refined Face-top 协调

由于属性的多样性、隐含的相关性以及一般偏好和个人偏好之间的权衡，存在于大多数服装试衣场景中的面对面协调具有挑战性。我们提出了一个基于深度的自我完善（DBSR）系统来模拟基于直觉评估的面部协调。为此，我们首先从时尚数据库和社区中建立了一个协调良好的 face-top (WCFT) 数据集。然后，我们使用联合训练的 CNN 深度典型相关分析 (DCCA) 方法来弥合基于 WCFT 数据集的语义面顶差距，以处理一般偏好。随后，开发了一种基于不相关性的最优路径森林 (OPF) 方法，以迭代地使结果适应个人偏好。实验结果和用户研究证明了我们方法的有效性。