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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/paul007pl/gmcnet
공식 코드Robust Partial-to-Partial Point Cloud Registration in a Full Range
https://github.com/pawelswoboda/LP_MP
공식 코드A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems
https://github.com/phmeier/smaragd
공식 코드SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance
https://github.com/phucty/MTab
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/phucty/mtab_tool
공식 코드MTab: Matching Tabular Data to Knowledge Graph using Probability Models
https://github.com/renmin1991/dyamic-graph-representation
공식 코드Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions
https://github.com/rucaibox/gmpt
공식 코드Neural Graph Matching for Pre-training Graph Neural Networks
https://github.com/rucaibox/ppgm
공식 코드https://github.com/ruizhang-ai/GMCF_Neural_Graph_Matching_based_Collaborative_Filtering
공식 코드https://github.com/rusty1s/deep-graph-matching-consensus
공식 코드https://github.com/salwaalkhatib/p2p-net
공식 코드https://github.com/seonghwanseo/molvoxel
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/seonghwanseo/pharmaconet
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/serend1p1ty/SeqNet
공식 코드https://github.com/shashanksule/esvd
공식 코드On the limits of neural network explainability via descrambling
https://github.com/skt9/clum-semantic-correspondence
공식 코드https://github.com/skunne/pointcloudregistration
공식 코드https://github.com/syxu828/Crosslingula-KG-Matching
공식 코드Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
https://github.com/taoyds/test-suite-sql-eval
공식 코드https://github.com/thinklab-sjtu/linsatnet
공식 코드LinSATNet: The Positive Linear Satisfiability Neural Networks
https://github.com/thinklab-sjtu/predictiveco-benchmark
공식 코드Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime
https://github.com/thinklab-sjtu/rgm
공식 코드Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
https://github.com/totilas/muffliato
공식 코드Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
https://github.com/tricktreat/promptner
공식 코드PromptNER: Prompt Locating and Typing for Named Entity Recognition
https://github.com/valeoai/FLOT
공식 코드FLOT: Scene Flow on Point Clouds Guided by Optimal Transport
https://github.com/vdvchen/sgmnet
공식 코드Learning to Match Features with Seeded Graph Matching Network
https://github.com/vislearn/gmbench
공식 코드A Comparative Study of Graph Matching Algorithms in Computer Vision
https://github.com/vislearn/libmpopt
공식 코드https://github.com/vislearn/multi-matching-optimization
공식 코드Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging
https://github.com/wangfudong/ZAC_GM
공식 코드https://github.com/wangguanan/HOReID
공식 코드High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
https://github.com/xbq1994/feature-recovery-transformer
공식 코드Learning Feature Recovery Transformer for Occluded Person Re-identification
https://github.com/xlearning-scu/2023-iccv-common
공식 코드https://github.com/xmed-lab/GraphEcho
공식 코드GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmlyqing00/clap
공식 코드CLAP: Concave Linear APproximation for Quadratic Graph Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.