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
@article{arxiv.1912.08967,
title = {Fashion Outfit Complementary Item Retrieval},
author = {Yen-Liang Lin and Son Tran and Larry S. Davis},
journal= {arXiv preprint arXiv:1912.08967},
year = {2020}
}