Dress Well via Fashion Cognitive Learning

TL;DR

This paper introduces a novel fashion cognitive learning framework that personalizes outfit recommendations by integrating visual-semantic outfit embeddings with individual appearance features, enhancing recommendation accuracy.

Contribution

It proposes the Fashion Cognitive Network (FCN), combining outfit encoding and graph neural networks to model personalized fashion recommendations based on personal physical data.

Findings

FCN outperforms existing methods on the O4U dataset.

The model effectively captures relationships between outfit features and personal appearance.

Experimental results demonstrate improved recommendation quality.

Abstract

Fashion compatibility models enable online retailers to easily obtain a large number of outfit compositions with good quality. However, effective fashion recommendation demands precise service for each customer with a deeper cognition of fashion. In this paper, we conduct the first study on fashion cognitive learning, which is fashion recommendations conditioned on personal physical information. To this end, we propose a Fashion Cognitive Network (FCN) to learn the relationships among visual-semantic embedding of outfit composition and appearance features of individuals. FCN contains two submodules, namely outfit encoder and Multi-label Graph Neural Network (ML-GCN). The outfit encoder uses a convolutional layer to encode an outfit into an outfit embedding. The latter module learns label classifiers via stacked GCN. We conducted extensive experiments on the newly collected O4U dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.