典型文献
Online Clothing Recommendation and Style Compatibility Learning Based on Joint Semantic Feature Fusion
文献摘要：
Clothing plays an important role in humans' social life as it can enhance people's personal quality, and it is a practical problem by answering the question "which item should be chosen to match current fashion items in a set to form collocational and compatible outfits". Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task. In detail, the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network (Mask-RCNN), respectively, so that the low-level texture information and high-level semantic information can be both preserved. Then, the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory (Bi-LSTM) for the prediction. Extensive simulations are conducted based on DeepFashion2 datasets. Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.
文献关键词：
中图分类号：
作者姓名：
FEI Yuzhe;SHANG Keke;ZHAO Mingbo;ZHANG Yue
作者机构：
College of Information Science and Technology,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitalized Textile&Fashion Technology,Donghua University,Shanghai 201620,China
文献出处：
引用格式：
[1]FEI Yuzhe;SHANG Keke;ZHAO Mingbo;ZHANG Yue-.Online Clothing Recommendation and Style Compatibility Learning Based on Joint Semantic Feature Fusion)[J].东华大学学报（英文版）,2022(04):325-331
A类：
collocational
B类：
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AB值：
0.673134
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
