Alireza Shafaei | علیرضا شفائی

selected publications

1. 

   AutoRetouch: Automatic Professional Face Retouching
   (Oral Presentation)

   Shafaei, Alireza, Schmidt, Mark, and Little, James J.

   In WACV 2021.

   Abstract Bib PDF Website YouTube FFHQR Dataset UBC CS

   Face retouching is one of the most time-consuming steps in professional photography pipelines. The existing automated approaches blindly apply smoothing on the skin, destroying the delicate texture of the face. We present the first automatic face retouching approach that produces high-quality professional-grade results in less than two seconds. Unlike previous work, we show that our method preserves textures and distinctive features while retouching the skin. We demonstrate that our trained models generalize across datasets and are suitable for low-resolution cellphone images. Finally, we release the first large-scale, professionally retouched dataset with our baseline to encourage further work on the presented problem.

   @inproceedings{Shafaei21,
     author = {Shafaei, Alireza and Schmidt, Mark and Little, James J.},
     title = {AutoRetouch: Automatic Professional Face Retouching},
     booktitle = {WACV},
     year = {2021},
     website = {https://openaccess.thecvf.com/content/WACV2021/html/Shafaei_AutoRetouch_Automatic_Professional_Face_Retouching_WACV_2021_paper.html},
     pdf = {https://openaccess.thecvf.com/content/WACV2021/papers/Shafaei_AutoRetouch_Automatic_Professional_Face_Retouching_WACV_2021_paper.pdf},
     dataset = {https://github.com/skylab-tech/ffhqr-dataset},
     dataset_name = {FFHQR},
     selected = {true},
     image = {papers/autoretouch.jpg},
     youtube = {https://www.youtube.com/watch?v=NBFkSL5XhHA},
     notes = {Oral Presentation},
     news = {https://www.cs.ubc.ca/news/2021/02/cs-alumnus-success-story-creating-picture-perfect-business-machine-learning},
     news_title = {UBC CS},
     bibtex_show = {true}
   }

2. 

   A Less Biased Evaluation of Out-of-distribution Sample Detectors
   (Spotlight)

   Shafaei, Alireza, Schmidt, Mark, and Little, James J.

   In BMVC 2019.

   Abstract arXiv Bib PDF Code Poster

   In the real world, a learning system could receive an input that is unlike anything it has seen during training. Unfortunately, out-of-distribution samples can lead to unpredictable behaviour. We need to know whether any given input belongs to the population distribution of the training/evaluation data to prevent unpredictable behaviour in deployed systems. A recent surge of interest in this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standard problem definition or an exhaustive evaluation, it is not evident if we can rely on these methods. What makes this problem different from a typical supervised learning setting is that the distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a more reliable strategy to assess progress on this problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Contrary to the existing results, we show that for realistic applications of high-dimensional images the previous techniques have low accuracy and are not reliable in practice.

   @inproceedings{Shafaei19,
     author = {Shafaei, Alireza and Schmidt, Mark and Little, James J.},
     title = {A Less Biased Evaluation of Out-of-distribution Sample Detectors},
     booktitle = {BMVC},
     year = {2019},
     pdf = {https://arxiv.org/pdf/1809.04729.pdf},
     code = {https://github.com/ashafaei/OD-test},
     arxiv = {1809.04729},
     selected = {true},
     image = {papers/odtest.jpg},
     poster = {poster/bmvc19_poster.pdf},
     notes = {Spotlight},
     bibtex_show = {true}
   }