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Graph Matching 벤치마크

Graph Matching on SPair-71k

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matching accuracy

0.6887 0.7293 0.7698 0.8104 0.851 2019-11 2026-09 NGM-v2 — 0.8067 (2019-11-26) NGM — 0.6887 (2019-11-26) BBGM — 0.8215 (2020-03-25) GCAN — 0.821 (2022-01-01) COMMON — 0.8454 (2022-12-08) GMTR — 0.832 (2023-11-14) GMT-BBGM — 0.8296 (2023-11-14) CREAM — 0.851 (2024-03-25) NGM-v2 — 0.8067 (2019-11-26) BBGM — 0.8215 (2020-03-25) COMMON — 0.8454 (2022-12-08) CREAM — 0.851 (2024-03-25)
RankModel matching accuracy PaperCodeYear
1 CREAM 0.851 Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining XLearning-SCU/2024-TIP-CREAM 2024
2 COMMON 0.8454 Graph Matching with Bi-level Noisy Correspondence Thinklab-SJTU/ThinkMatch · Lin-Yijie/Graph-Matching-Networks · xlearning-scu/2023-iccv-common 2022
3 GMTR 0.832 GMTR: Graph Matching Transformers jp-guo/gm-transformer 2023
4 GMT-BBGM 0.8296 GMTR: Graph Matching Transformers jp-guo/gm-transformer 2023
5 BBGM 0.8215 Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers Thinklab-SJTU/ThinkMatch · martius-lab/blackbox-backprop · martius-lab/blackbox-deep-graph-matching · +2 2020
6 GCAN 0.8210 Graph-Context Attention Networks for Size-Varied Deep Graph Matching zhehengjiang/gcan 2022
7 NGM-v2 0.8067 Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching Thinklab-SJTU/ThinkMatch 2019
8 NGM 0.6887 Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching Thinklab-SJTU/ThinkMatch 2019
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