东华大学学报（英文版）2024，Vol.41Issue(1)：21-27,7.DOI:10.19884/j.1672-5220.202302017
基于层次化多模态注意力机制循环神经网络的服装新品销售预测
Sales Forecasting of New Clothing Products Based on Hierarchical Multi-Modal Attention Recurrent Neural Network
石闻达 1杜劲松 2李笛出乘3
作者信息
- 1. 东华大学 服装与艺术设计学院,上海 200051
- 2. 东华大学 服装与艺术设计学院,上海 200051||新疆大学 纺织与服装学院,新疆 乌鲁木齐 830046
- 3. 复旦大学 计算机科学技术学院,上海 200433
- 折叠
摘要
Abstract
In the task of sales forecasting of new clothing products,the lack of historical sales data often necessitates the full utilization of data from other modalities as a supplement.However,multi-modal clothing data are usually redundant and heterogeneous.To solve the problems,a hierarchical multi-modal attention based recurrent neural network(HMA-RNN)including three main elements is proposed.The hierarchical structure separates high-level semantic information from low-level semantic information to avoid information redundancy.The multi-modal attention(MMA)is introduced in the fusion stage to mitigate inherent data non-alignment.The shared attention mechanism is utilized to build the dependencies across the multi-modal data.Experimental results on the Visuelle 2.0 dataset show that the proposed approach achieves promising results with 72.07 on the weighted average percentage error(WAPE)and 0.80 on the mean absolute error(MAE),outperforming existing works significantly,which indicates the effectiveness of the proposed approach.关键词
服装销售预测/多模态学习/深度学习/注意力机制(MMA)Key words
clothing sales forecasting/multi-modal learning/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
石闻达,杜劲松,李笛出乘..基于层次化多模态注意力机制循环神经网络的服装新品销售预测[J].东华大学学报（英文版）,2024,41(1):21-27,7.
