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Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

arXiv 2003.11657 ↗First posted 25 Mar 2020cs.LG stat.MLcited by 81

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Abstract (arXiv)

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. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matching

Key facts. Cited by 81 papers in the map (data through 13 Oct 2026). First posted on arXiv 25 Mar 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 15; 2022: 21; 2023: 19; 2024: 18; 2025: 5; 2026: 3.

81 papers.

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  1. 2023Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunitiescited by 130
  2. 2021Implicit MLE: Backpropagating Through Discrete Exponential Family Distributionscited by 98
  3. 2022Relationformer: A Unified Framework for Image-to-Graph Generationcited by 67
  4. 2023LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matchingcited by 60
  5. 2021CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraintscited by 49
  6. 2021Convolutional Hough Matching Networkscited by 43
  7. 2023SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matchingcited by 38
  8. 2022SIMPLE: A Gradient Estimator for k-Subset Samplingcited by 34
  9. 2021GREED: A Neural Framework for Learning Graph Distance Functionscited by 32
  10. 2022Graph Matching with Bi-level Noisy Correspondencecited by 30
  11. 2022Backpropagation through Combinatorial Algorithms: Identity with Projection Workscited by 29
  12. 2022Densely Constrained Depth Estimator for Monocular 3D Object Detectioncited by 27
38 more
  1. 2023Jigsaw: Learning to Assemble Multiple Fractured Objectscited by 26
  2. 2022SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problemscited by 21
  3. 2021Deep Graph Matching under Quadratic Constraintcited by 18
  4. 2021Fusion Moves for Graph Matchingcited by 16
  5. 2024LinSATNet: The Positive Linear Satisfiability Neural Networkscited by 16
  6. 2022A Comparative Study of Graph Matching Algorithms in Computer Visioncited by 14
  7. 2022Equivariant Hypergraph Neural Networkscited by 14
  8. 2023Factor Graph Neural Networkscited by 13
  9. 2021Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondencecited by 12
  10. 2022Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Modelscited by 12
  11. 2021Stochastic Iterative Graph Matchingcited by 10
  12. 2021Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approachcited by 10
  13. 2023Assessing SATNet's Ability to Solve the Symbol Grounding Problemcited by 10
  14. 2023IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiationcited by 9
  15. 2021Joint Graph Learning and Matching for Semantic Feature Correspondencecited by 8
  16. 2022Total Variation Optimization Layers for Computer Visioncited by 8
  17. 2022Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networkscited by 8
  18. 2024GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descentcited by 7
  19. 2022Efficient Linear Attention for Fast and Accurate Keypoint Matchingcited by 6
  20. 2024Ensemble Quadratic Assignment Network for Graph Matchingcited by 6
  21. 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layerscited by 6
  22. 2024ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Modelscited by 6
  23. 2022A Solver-Free Framework for Scalable Learning in Neural ILP Architecturescited by 5
  24. 2021Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collectionscited by 3
  25. 2021Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?cited by 3
  26. 2021One-shot Key Information Extraction from Document with Deep Partial Graph Matchingcited by 3
  27. 2022SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matchingcited by 3
  28. 2022Kernelized multi-graph matchingcited by 3
  29. 2022QuAnt: Quantum Annealing with Learnt Couplingscited by 3
  30. 2022Universe Points Representation Learning for Partial Multi-Graph Matchingcited by 3
  31. 2023AMatFormer: Efficient Feature Matching via Anchor Matching Transformercited by 3
  32. 2023GenCO: Generating Diverse Designs with Combinatorial Constraintscited by 3
  33. 2022Learning Universe Model for Partial Matching Networks over Multiple Graphscited by 2
  34. 2023Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rayscited by 2
  35. 2023Unsupervised Deep Graph Matching Based on Cycle Consistencycited by 2
  36. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  37. 2024Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimagingcited by 2
  38. 2024Learning Partial Graph Matching via Optimal Partial Transportcited by 2

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