摘要
为优化消费者的定制体验,提升传统汉服定制推荐的效率,依据文本生成图片原理,提出一种基于改进堆叠生成对抗网络的智能定制推荐方法,该方法由2个生成对抗网络模型构成。首先构建基于嵌入式-长短期记忆网络(Embedding and Long Short-Term Memory,Embedding-LSTM)的需求文本编码模型,改进原模型存在的文本编码稀疏孤立问题;其次引入残差结构,提升汉服文图特征提取和传递能力,最后采用平均绝对误差构建损失函数优化学习过程,提升文本描述与生成图像的一致性。结果表明:与原模型相比,由改进模型所生成的汉服图像更逼真,质量更优,细节处理更精细,主客观评估结果证明模型改进的有效性;与ERNIE-ViLG、Composer和传统的Diffusion Model等大模型方法相比,所提方法在匹配需求文本和生成真实性方面表现更好,显示了其适用性。
Objective In order to improve the communication efficiency and optimize the customization experience during the process of customizing Hanfu for users,an intelligent customization recommendation method based on an improved stack-generative adversarial network is proposed,relying on the technology of text generated images.Using this method,the user′s customized requirement text will be directly generated into Hanfu sample images for reference,efficiently achieving personalized recommendations based on the user′s customized needs.Method The improvement method consisted of a two-stage model.In the first stage,a demand text encoding model based on Embeddding and Long Short-Term Memory(Embedding-LSTM)was constructed to address the problem of sparse and isolated text encoding in the original model.In the second stage,residual structures were introduced to optimize the feature extraction and transmission capabilities of Hanfu text images,providing better guide to the the second stage training process based on the primary features learned in the first stage.Then,the average absolute error was used to construct a loss function to optimize the learning process,thereby improving the consistency between text description and generated images.Results A dataset comprising 8156 Hanfu item images was established through data collection and enhancement techniques.By further subdividing the traditional Hanfu into 70 subdimensions based on 7 attributes,which are dynasty,category,collar shape,sleeve shape,color scheme,season,and image presentation,a learning and mapping model was established from customized demand text to Hanfu images.To determine the authenticity of the generated Hanfu images,the Inception V3 model was utilized to calculate the Fr chet Incepton Distance(FID value).To effectively evaluate the quality of the generated Hanfu images,the pretrained Hanfu fine-grained classification model was used to calculate the second FID value.The objective evaluation results revealed that after the improvement of the model,the differences between the two FID values dropped from 35.86%to 10.59%respectively,indicating that the Hanfu images generated by the improved model had stronger discriminability and were closer to real Hanfu compared to the original model.38 respondents were invited to participate in a subjective evaluation,and it was found that the Hanfu images generated by the improved model scored higher than the real images in terms of similarity,structural integrity,overall beauty,and text matching.This indicated that the images generated by the improved model were more realistic and favored than those generated by the original model,with more complete and aesthetic style results and higher compatibility with text labels.Comparative experiments were conducted with large model methods such as the Transformer-based ERNIE ViLG diffusion model,the U-Net convolutional neural network-based Composer,and traditional Diffusion Models.The results showed that the improved method proposed in this research had higher FID values when calculated using the Inception V3 model,while the FID values calculated using the Hanfu fine-grained classification model had the lowest scores.This indicated that the Hanfu generated by the proposed method contained more traditional Chinese clothing elements,suggesting that the proposed method was more suitable for the generation of traditional Hanfu.Conclusion This research focused on Hanfu customization recommendation and proposed an intelligent recommendation method based on an improved stack-generative adversarial network.Research has shown that the improved method is able to produce more realistic and high-quality Hanfu images,and performs better in matching requirement texts,demonstrating the effectiveness and applicability of the proposed method in generating Hanfu images.This study only selected traditional Hanfu as the research object.In future research,more comprehensive Hanfu text and image data will be collected and organized to improve the robustness and diversity of model generation,enhance Hanfu recommendation satisfaction,and optimize customization experience.In addition,future research can also consider combining improved methods with large-scale modeling techniques to continuously optimize and improve Hanfu customization recommendation technology.
作者
蔡丽玲
王梅
邵一兵
陈炜
曹华卿
季晓芬
CAI Liling;WANG Mei;SHAO Yibing;CHEN Wei;CAO Huaqing;JI Xiaofen(School of International Education,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;School of Fashion Design&Engineering,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;College of Textile Science and Engineering,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;China National Silk Museum,Hangzhou,Zhejiang 310002,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2024年第12期180-188,共9页
Journal of Textile Research
基金
浙江省哲学社会科学规划项目(24NDJC170YB)
浙江理工大学科研基金启动项目(23196023-Y)。
关键词
深度学习
生成对抗网络
汉服定制
智能推荐
生成模型
deep learning
generative adversarial network
Hanfu customization
intelligent recommendation
generative model
作者简介
第一作者:蔡丽玲(1980-),女,副教授,博士。主要研究方向为服装个性化定制;通信作者:季晓芬(1971-),女,教授,博士。主要研究方向为服装个性化定制与智能制造。E-mail:xiaofenji@zstu.edu.cn。
