Dress Well via Fashion Cognitive Learning

Kaicheng PANG (Hong Kong Polytechnic University), Xingxing Zou (Laboratory for Artificial Intelligence in Design, The Hong Kong Polytechnic University), Waikeung Wong (Institute of Textiles and Clothing, The Hong Kong Polytechnic University)*
The 33rd British Machine Vision Conference


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 Convolutional 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 Convolutional Networks (ML-GCN). The outfit encoder uses a convolutional layer to encode an outfit into an outfit embedding. The latter module learns classifiers for physical labels via stacked GCN. We build a new dataset named Outfit for You (O4U) that contains 29,352 valid outfits with 5.25 unmatched physical labels on average. Extensive experiments are conducted on the O4U dataset and the quantitative results on O4U show that our proposed approach outperforms alternative approaches by clear margins.



author    = {Kaicheng PANG and Xingxing Zou and Waikeung Wong},
title     = {Dress Well via Fashion Cognitive Learning},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {https://bmvc2022.mpi-inf.mpg.de/0251.pdf}

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