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Models/Graphormer

Graphormer

Reported on 7 benchmarks across 6 tasks · 2 papers · 3 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Graphs3 results

  • Graph RegressiononPCQM4Mv2-LSC
    Validation MAE· 2021-06-09
    0.0864
    best: 0.0235 (ESA (Edge set attention, no positional encodings))
    SOTA
    Do Transformers Really Perform Bad for Graph Representation?arXiv:2106.05234
  • Graph Property Predictiononogbg-molhiv
    Number of params· 2021-06-09
    47183040
    SOTA
    Do Transformers Really Perform Bad for Graph Representation?arXiv:2106.05234
  • Graph Property Predictiononogbg-molpcba
    Number of params· 2021-06-09
    119529664
    best: 119529665 (HIG(pre-trained on PCQM4M))
    SOTA
    Do Transformers Really Perform Bad for Graph Representation?arXiv:2106.05234

Computer Vision2 results

  • 3D Human Pose EstimationonSLOPER4D
    Average MPJPE (mm)· 2023-11-20
    77.1
    best: 86.06 (LiDARCap)
    LiDAR-HMR: 3D Human Mesh Recovery from LiDARarXiv:2311.11971
  • Pose EstimationonSLOPER4D
    Average MPJPE (mm)· 2023-11-20
    77.1
    best: 86.06 (LiDARCap)
    LiDAR-HMR: 3D Human Mesh Recovery from LiDARarXiv:2311.11971

Methodology1 result

  • 3DonSLOPER4D
    Average MPJPE (mm)· 2023-11-20
    77.1
    best: 86.06 (LiDARCap)
    LiDAR-HMR: 3D Human Mesh Recovery from LiDARarXiv:2311.11971

Audio1 result

  • 1 Image, 2*2 StitchionSLOPER4D
    Average MPJPE (mm)· 2023-11-20
    77.1
    best: 86.06 (LiDARCap)
    LiDAR-HMR: 3D Human Mesh Recovery from LiDARarXiv:2311.11971