@inproceedings{rolinek2020:deepgraphmatching,
  title = {Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers},
  booktitle = {Computer Vision – ECCV 2020},
  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 the 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.},
  volume = {28},
  pages = {407--424},
  series = {Lecture Notes in Computer Science, 12373},
  editors = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael},
  publisher = {Springer},
  address = {Cham},
  month = aug,
  year = {2020},
  slug = {rolinek2020-deepgraphmatching},
  author = {Rolínek, Michal and Swoboda, Paul and Zietlow, Dominik and Paulus, Anselm and Musil, Vít and Martius, Georg},
  month_numeric = {8}
}