In this article, we develop an end-to-end clothing collocation learning framework based on a bidirectional long short-term memories (Bi-LTSM) model, and propose new feature extraction and fusion modules. The feature extraction module uses Inception V3 to extract low-level feature information and the segmentation branches of Mask Region Convolutional Neural Network (RCNN) to extract high-level semantic information; whereas the feature fusion module creates a new reference vector for each image to fuse the two types of image feature information. As a result, the feature can involve both low-level image and high-level semantic feature information, so that the performance of Bi-LSTM can be enhanced. Extensive simulations are conducted based on Ployvore and DeepFashion2 datasets. Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.

中文翻译：

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基于语义特征融合的服装搭配的端到端框架

在本文中，我们基于双向长期短期记忆（Bi-LTSM）模型开发了端到端的服装搭配学习框架，并提出了新的特征提取和融合模块。特征提取模块使用Inception V3提取低级特征信息，并使用遮罩区域卷积神经网络（RCNN）的分割分支提取高级语义信息。特征融合模块为每个图像创建新的参考矢量，以融合两种类型的图像特征信息。结果，该特征可以同时包含低级图像和高级语义特征信息，从而可以增强Bi-LSTM的性能。基于Ployvore和DeepFashion2数据集进行了广泛的模拟。