https://github.com/BinruiShen/CSGO
공식 코드CSGO: Constrained-Softassign Gradient Optimization For Large Graph Matching
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Graph Matching is the problem of finding correspondences between two sets of vertices while preserving complex relational information among them. Since the graph structure has a strong capacity to represent objects and robustness to severe deformation and outliers, it is frequently adopted to formulate various correspondence problems in the field of computer vision. Theoretically, the Graph Matching problem can be solved by exhaustively searching the entire solution space. However, this approach is infeasible in practice because the solution space expands exponentially as the size of input data increases. For that reason, previous studies have attempted to solve the problem by using various approximation techniques.
Source: Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CSGO: Constrained-Softassign Gradient Optimization For Large Graph Matching
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Knowledge Generation -- Variational Bayes on Knowledge Graphs
Joint Graph Learning and Matching for Semantic Feature Correspondence
Dynamic Graph Representation for Partially Occluded Biometrics
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
Learning Combinatorial Embedding Networks for Deep Graph Matching
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns
HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Densely Constrained Depth Estimator for Monocular 3D Object Detection
SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity
Template based Graph Neural Network with Optimal Transport Distances
Learning without Isolation: Pathway Protection for Continual Learning
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.