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Models/LPIPS (VGG)

LPIPS (VGG)

Reported on 9 benchmarks across 3 tasks · 1 paper

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

Computer Vision6 results

  • Video UnderstandingonMSU SR-QA Dataset
    KLCC· 2018-01-11
    0.41471
    best: 0.69774 (ClipIQA+)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • Video UnderstandingonMSU SR-QA Dataset
    PLCC· 2018-01-11
    0.5282
    best: 0.75743 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • Video UnderstandingonMSU SR-QA Dataset
    SROCC· 2018-01-11
    0.52868
    best: 0.75215 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • VideoonMSU SR-QA Dataset
    KLCC· 2018-01-11
    0.41471
    best: 0.69774 (ClipIQA+)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • VideoonMSU SR-QA Dataset
    PLCC· 2018-01-11
    0.5282
    best: 0.75743 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • VideoonMSU SR-QA Dataset
    SROCC· 2018-01-11
    0.52868
    best: 0.75215 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924

Time Series3 results

  • Video Quality AssessmentonMSU SR-QA Dataset
    KLCC· 2018-01-11
    0.41471
    best: 0.69774 (ClipIQA+)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • Video Quality AssessmentonMSU SR-QA Dataset
    PLCC· 2018-01-11
    0.5282
    best: 0.75743 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924
  • Video Quality AssessmentonMSU SR-QA Dataset
    SROCC· 2018-01-11
    0.52868
    best: 0.75215 (PieAPP)
    The Unreasonable Effectiveness of Deep Features as a Perceptual MetricarXiv:1801.03924