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An End-to-End Framework for Clothing Collocation Based on Semantic Feature Fusion
IEEE Multimedia ( IF 2.3 ) Pub Date : 2020-09-17 , DOI: 10.1109/mmul.2020.3024221
Mingbo Zhao 1 , Yu Liu 1 , Xianrui Li 1 , Zhao Zhang 2 , Yue Zhang 1
Affiliation  

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:

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。
更新日期:2020-11-25
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