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Models/LINEARITY

LINEARITY

Reported on 12 benchmarks across 4 tasks · 1 paper

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

Computer Vision9 results

  • Video UnderstandingonMSU NR VQA Database
    KLCC· 2020-08-10
    0.7589
    best: 0.7883 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video UnderstandingonMSU NR VQA Database
    PLCC· 2020-08-10
    0.9106
    best: 0.9431 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video UnderstandingonMSU NR VQA Database
    SRCC· 2020-08-10
    0.9104
    best: 0.9289 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Image Quality AssessmentonMSU NR VQA Database
    KLCC· 2020-08-10
    0.7589
    best: 0.7648 (UNIQUE)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Image Quality AssessmentonMSU NR VQA Database
    PLCC· 2020-08-10
    0.9106
    best: 0.9238 (UNIQUE)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Image Quality AssessmentonMSU NR VQA Database
    SRCC· 2020-08-10
    0.9104
    best: 0.9148 (UNIQUE)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU NR VQA Database
    KLCC· 2020-08-10
    0.7589
    best: 0.7883 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU NR VQA Database
    PLCC· 2020-08-10
    0.9106
    best: 0.9431 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • VideoonMSU NR VQA Database
    SRCC· 2020-08-10
    0.9104
    best: 0.9289 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889

Time Series3 results

  • Video Quality AssessmentonMSU NR VQA Database
    KLCC· 2020-08-10
    0.7589
    best: 0.7883 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video Quality AssessmentonMSU NR VQA Database
    PLCC· 2020-08-10
    0.9106
    best: 0.9431 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889
  • Video Quality AssessmentonMSU NR VQA Database
    SRCC· 2020-08-10
    0.9104
    best: 0.9289 (MDTVSFA)
    Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality AssessmentarXiv:2008.03889