Optimizing Rank-based Metrics with Blackbox Differentiation
2020
Conference Paper
al
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.
Author(s): | Michal Rolínek and Vít Musil and Anselm Paulus and Marin Vlastelica and Claudio Michaelis and Georg Martius |
Book Title: | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) |
Pages: | 7617 -- 7627 |
Year: | 2020 |
Month: | June |
Day: | 14-19 |
Publisher: | IEEE |
Department(s): | Autonomous Learning |
Research Project(s): |
Combinatorial Optimization as a Layer / Blackbox Differentiation
|
Bibtex Type: | Conference Paper (inproceedings) |
Paper Type: | Conference |
DOI: | 10.1109/CVPR42600.2020.00764 |
Event Name: | IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR 2020) |
Event Place: | Seattle, WA, USA |
Address: | Piscataway, NJ |
Digital: | True |
ISBN: | 978-1-7281-7168-5 |
Note: | Best paper nomination |
State: | Published |
Talk Type: | Oral |
URL: | https://openaccess.thecvf.com/content_CVPR_2020/html/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.html |
Links: |
Paper @ CVPR2020
Long Oral Short Oral Arxiv Code |
Video: | |
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BibTex @inproceedings{Rolinek2020optimizing, title = {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}, booktitle = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)}, pages = {7617 -- 7627}, publisher = {IEEE}, address = {Piscataway, NJ}, month = jun, year = {2020}, note = {Best paper nomination}, doi = {10.1109/CVPR42600.2020.00764}, url = {https://openaccess.thecvf.com/content_CVPR_2020/html/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.html}, month_numeric = {6} } |