Title: A deep neural network for fashion retrieval based on multi-attention attribute manipulation

Authors: Qianyi Liu; Jiaohua Qin; Xuyu Xiang; Yun Tan

Addresses: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China ' College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China

Abstract: The surge in online shopping has heightened the demand for interactive fashion design retrieval. Existing methods, however, exhibit imperfections in attribute segmentation, attributed to the specificity of clothing attributes. The attention region often encounters multiple attributes overlapping, causing changes in one attribute to affect irrelevant ones, resulting in poor retrieval accuracy. This paper addresses this challenge by proposing a deep neural network for fashion retrieval based on multi-attention attribute manipulation. In this approach, the feature extraction module sifts the extracted features to obtain an overall description of the clothing image by adding ESE-NAM combined attention modules to the VoVNet network block. The attribute decoding module utilises one-hot coding and feature mapping to subdivide the attribute features, obtaining more independent local detail features for refined attribute image retrieval with a focus on details. Experimental results show that the proposed network surpasses existing networks with an overall accuracy increase of more than 4% points, particularly with the feature extraction module demonstrating an accuracy boost of over 6% points.

Keywords: image retrieval; fashion design retrieval; interactive image retrieval; deep neural network; deep learning.

DOI: 10.1504/IJAACS.2025.148532

International Journal of Autonomous and Adaptive Communications Systems, 2025 Vol.18 No.4, pp.341 - 356

Received: 29 Nov 2023
Accepted: 15 Jan 2024
Published online: 11 Sep 2025 *

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