Publication details
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| MU Faculty or unit | |
| Citation | |
| Web | Springer |
| Doi | http://dx.doi.org/10.1007/978-3-030-58604-1_25 |
| Keywords | Combinatorial optimization; Deep graph matching; Keypoint correspondence |
| Description | 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. |