Name
Abstract | ||
|---|---|---|
Learning the compatibility relationship is of vital importance to a fashion recommendation system, while existing works achieve this merely on product images but not on street images in the complex daily life scenario. In this paper, we propose a novel fashion recommendation system: Given a query item of interest in the street scenario, the system can return the compatible items. More specifically, a two-stage curriculum learning scheme is developed to transfer the semantics from the product to street outfit images. We also propose a domain-specific missing item imputation method based on style and color similarity to handle the incomplete outfits. To support the training of deep recommendation model, we collect a large dataset with street outfit images. The experiments on the dataset demonstrate the advantages of the proposed method over the state-of-the-art approaches on both the street images and the product images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICIP.2019.8802939 | 2019 IEEE International Conference on Image Processing (ICIP) |
Keywords | DocType | ISSN |
Fashion Recommendation,Outfit Completion,Street Photos,Curriculum Learning | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-5386-6250-2 | 0 | 0.34 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijing Zhan | 1 | 7 | 3.21 |
| Boxin Shi | 2 | 381 | 45.76 |
| Chen Jiawei | 3 | 57 | 11.60 |
| Qian Zheng | 4 | 44 | 13.91 |
| Ling-yu Duan | 5 | 1770 | 124.87 |
| Alex C. Kot | 6 | 1096 | 92.07 |