Informace o publikaci
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
| Autoři | |
|---|---|
| Rok publikování | 2020 |
| Druh | Článek ve sborníku |
| Konference | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | Springer |
| Doi | https://doi.org/10.1007/978-3-030-58604-1_25 |
| Klíčová slova | Combinatorial optimization; Deep graph matching; Keypoint correspondence |
| Popis | 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. |