Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Abstract
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups.
Cite
Text
Rolínek et al. "Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58604-1_25Markdown
[Rolínek et al. "Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/rolinek2020eccv-deep/) doi:10.1007/978-3-030-58604-1_25BibTeX
@inproceedings{rolinek2020eccv-deep,
title = {{Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers}},
author = {Rolínek, Michal and Swoboda, Paul and Zietlow, Dominik and Paulus, Anselm and Musil, Vít and Martius, Georg},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58604-1_25},
url = {https://mlanthology.org/eccv/2020/rolinek2020eccv-deep/}
}