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Models/Entry-V2

Entry-V2

Reported on 48 benchmarks across 8 tasks · 1 paper · 16 SOTA

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

Computer Vision24 results

  • Depth EstimationonOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Depth EstimationonOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Face ReconstructiononOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Face ReconstructiononOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3D Face ReconstructiononOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3D Face ReconstructiononOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Depth And Camera MotiononOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Depth And Camera MotiononOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Depth EstimationonReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • Depth EstimationonReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • Depth EstimationonMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • Depth EstimationonMSU-MFSD
    HTER· uses extra data
    0
  • Face ReconstructiononReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • Face ReconstructiononReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • Face ReconstructiononMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • Face ReconstructiononMSU-MFSD
    HTER· uses extra data
    0
  • 3D Face ReconstructiononReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • 3D Face ReconstructiononReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • 3D Face ReconstructiononMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • 3D Face ReconstructiononMSU-MFSD
    HTER· uses extra data
    0
  • Depth And Camera MotiononReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • Depth And Camera MotiononReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • Depth And Camera MotiononMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • Depth And Camera MotiononMSU-MFSD
    HTER· uses extra data
    0

Music6 results

  • Facial Recognition and ModellingonOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Facial Recognition and ModellingonOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Facial Recognition and ModellingonReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • Facial Recognition and ModellingonReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • Facial Recognition and ModellingonMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • Facial Recognition and ModellingonMSU-MFSD
    HTER· uses extra data
    0

Robots6 results

  • Visual OdometryonOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Visual OdometryonOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • Visual OdometryonReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • Visual OdometryonReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • Visual OdometryonMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • Visual OdometryonMSU-MFSD
    HTER· uses extra data
    0

Methodology6 results

  • 3DonOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3DonOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3DonReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • 3DonReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • 3DonMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • 3DonMSU-MFSD
    HTER· uses extra data
    0

Medical6 results

  • 3D Face ModellingonOULU-NPU
    ACER· uses extra data· 2022-06-13
    3.2
    best: 2.92 (Bi-FPNFAS)
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3D Face ModellingonOULU-NPU
    HTER· uses extra data· 2022-06-13
    2.6
    SOTA
    Generalizable Method for Face Anti-Spoofing with Semi-Supervised LearningarXiv:2206.06510
  • 3D Face ModellingonReplay-Attack
    EER· uses extra data
    0
    best: 2.14 (Multi-Scale)
  • 3D Face ModellingonReplay-Attack
    HTER· uses extra data
    0
    best: 2.9 (YCbCr+HSV-LBP)
  • 3D Face ModellingonMSU-MFSD
    Equal Error Rate· uses extra data
    0
  • 3D Face ModellingonMSU-MFSD
    HTER· uses extra data
    0