B2-PFR: Deep bidirectional learning of user-item body features for fashion compatibility modeling.
- DOI
- 10.1016/j.neunet.2026.109360
- Published
- 2027 Jan
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109360,
title = {B2-PFR: Deep bidirectional learning of user-item body features for fashion compatibility modeling.},
author = {Ding W and Huang X and Zhang S},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109360},
url = {https://doi.org/10.1016/j.neunet.2026.109360}
}RIS
TY - JOUR TI - B2-PFR: Deep bidirectional learning of user-item body features for fashion compatibility modeling. AU - Ding W AU - Huang X AU - Zhang S PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109360 UR - https://doi.org/10.1016/j.neunet.2026.109360 ER -
APA
W, D., X, H., & S, Z. (2027). B2-PFR: Deep bidirectional learning of user-item body features for fashion compatibility modeling.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109360
Source records
- pubmed · retrieved 2026-09-26T18:52:53.303Z