Scalable and Explainable Outfit Generation

Alexander Lorbert, David Neiman, Arik Poznanski, Eduard Oks, Larry Davis

CVPRW 2021 pp. 3931-3934

doi:10.1109/CVPRW53098.2021.00439 /cvprw/2021/lorbert2021cvprw-scalable/

Abstract

We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category to a visual descriptor output. This output is then fed into nearest-neighbor-based visual search, which, during training, is learned via triplet loss and mini-batch retrievals. At inference time, we use a beam search with a desired outfit composition to generate outfits at scale. Moreover, the attention networks provide a diagnostic look into the recommendation process, serving as a fashion-based sanity check.

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Cite

Text

Lorbert et al. "Scalable and Explainable Outfit Generation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00439

Markdown

[Lorbert et al. "Scalable and Explainable Outfit Generation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/lorbert2021cvprw-scalable/) doi:10.1109/CVPRW53098.2021.00439

BibTeX

@inproceedings{lorbert2021cvprw-scalable,
  title     = {{Scalable and Explainable Outfit Generation}},
  author    = {Lorbert, Alexander and Neiman, David and Poznanski, Arik and Oks, Eduard and Davis, Larry},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2021},
  pages     = {3931-3934},
  doi       = {10.1109/CVPRW53098.2021.00439},
  url       = {https://mlanthology.org/cvprw/2021/lorbert2021cvprw-scalable/}
}