https://github.com/xwasco/DominantSetLibrary
공식 코드DSLib: An open source library for the dominant set clustering method
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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.
DSLib: An open source library for the dominant set clustering method
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
MolGAN: An implicit generative model for small molecular graphs
MolGAN: An implicit generative model for small molecular graphs
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
MolGAN: An implicit generative model for small molecular graphs
MolGAN: An implicit generative model for small molecular graphs
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
VLG-Net: Video-Language Graph Matching Network for Video Grounding
MolGAN: An implicit generative model for small molecular graphs
MolGAN: An implicit generative model for small molecular graphs
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.