Fashion Outfit Complementary Item Retrieval
Abstract
Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting. We propose a new framework for outfit complementary item retrieval. Specifically, a category-based subspace attention network is presented, which is a scalable approach for learning the subspace attentions. In addition, we introduce an outfit ranking loss that better models the item relationships of an entire outfit. We evaluate our method on the outfit compatibility, FITB and new retrieval tasks. Experimental results demonstrate that our approach outperforms state-of-the-art methods in both compatibility prediction and complementary item retrieval.
Cite
Text
Lin et al. "Fashion Outfit Complementary Item Retrieval." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00337Markdown
[Lin et al. "Fashion Outfit Complementary Item Retrieval." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/lin2020cvpr-fashion/) doi:10.1109/CVPR42600.2020.00337BibTeX
@inproceedings{lin2020cvpr-fashion,
title = {{Fashion Outfit Complementary Item Retrieval}},
author = {Lin, Yen-Liang and Tran, Son and Davis, Larry S.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020},
doi = {10.1109/CVPR42600.2020.00337},
url = {https://mlanthology.org/cvpr/2020/lin2020cvpr-fashion/}
}