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Models/KonCept512

KonCept512

Reported on 13 benchmarks across 4 tasks · 2 papers · 1 SOTA

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

Computer Vision10 results

  • Image Quality AssessmentonKonIQ-10k
    SRCC· 2018-03-22
    0.921
    best: 0.948 (RealQA)
    SOTA
    KonIQ-10k: Towards an ecologically valid and large-scale IQA databasearXiv:1803.08489
  • Video UnderstandingonMSU NR VQA Database
    KLCC· 2019-10-14
    0.6608
    best: 0.7883 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Video UnderstandingonMSU NR VQA Database
    PLCC· 2019-10-14
    0.8464
    best: 0.9431 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Video UnderstandingonMSU NR VQA Database
    SRCC· 2019-10-14
    0.836
    best: 0.9289 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Image Quality AssessmentonMSU NR VQA Database
    KLCC· 2019-10-14
    0.6608
    best: 0.7648 (UNIQUE)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Image Quality AssessmentonMSU NR VQA Database
    PLCC· 2019-10-14
    0.8464
    best: 0.9238 (UNIQUE)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Image Quality AssessmentonMSU NR VQA Database
    SRCC· 2019-10-14
    0.836
    best: 0.9148 (UNIQUE)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • VideoonMSU NR VQA Database
    KLCC· 2019-10-14
    0.6608
    best: 0.7883 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • VideoonMSU NR VQA Database
    PLCC· 2019-10-14
    0.8464
    best: 0.9431 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • VideoonMSU NR VQA Database
    SRCC· 2019-10-14
    0.836
    best: 0.9289 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180

Time Series3 results

  • Video Quality AssessmentonMSU NR VQA Database
    KLCC· 2019-10-14
    0.6608
    best: 0.7883 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Video Quality AssessmentonMSU NR VQA Database
    PLCC· 2019-10-14
    0.8464
    best: 0.9431 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180
  • Video Quality AssessmentonMSU NR VQA Database
    SRCC· 2019-10-14
    0.836
    best: 0.9289 (MDTVSFA)
    KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentarXiv:1910.06180