@inproceedings{Rolinek2020optimizing,
  title = {Optimizing Rank-based Metrics with Blackbox Differentiation},
  booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)},
  abstract = {Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors. },
  pages = {7617 -- 7627},
  publisher = {IEEE},
  address = {Piscataway, NJ},
  month = jun,
  year = {2020},
  note = {Best paper nomination},
  slug = {optimizing-rank-based-metrics-with-blackbox-differentiation},
  author = {Rolínek, Michal and Musil, Vít and Paulus, Anselm and Vlastelica, Marin and Michaelis, Claudio and Martius, Georg},
  url = {https://openaccess.thecvf.com/content_CVPR_2020/html/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.html},
  month_numeric = {6}
}