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Models/PGL-SUM

PGL-SUM

Reported on 10 benchmarks across 2 tasks

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

Computer Vision14 results

  • VideoonTvSum
    F1-score (Canonical)· uses extra data
    61
    best: 67.5 (MAVS [DBLP:conf/mm/FengLKZ18])
  • VideoonMr. HiSum
    MAP (50%)
    61.6
  • VideoonSumMe
    F1-score (Canonical)
    55.6
    best: 57.1 (PGL-SUM (maximum learning capacity))
  • VideoonTvSum
    F1-score (Canonical)
    61
    best: 67.5 (MAVS [DBLP:conf/mm/FengLKZ18])
  • VideoonTvSum
    Kendall's Tau
    0.157
    best: 0.203 (DMASum)
  • VideoonTvSum
    Spearman's Rho
    0.206
    best: 0.267 (DMASum)
  • VideoonSumMe
    F1-score (Canonical)
    55.6
    best: 57.1 (PGL-SUM (maximum learning capacity))
  • Video SummarizationonTvSum
    F1-score (Canonical)· uses extra data
    61
    best: 67.5 (MAVS [DBLP:conf/mm/FengLKZ18])
  • Video SummarizationonMr. HiSum
    MAP (50%)
    61.6
  • Video SummarizationonSumMe
    F1-score (Canonical)
    55.6
    best: 57.1 (PGL-SUM (maximum learning capacity))
  • Video SummarizationonTvSum
    F1-score (Canonical)
    61
    best: 67.5 (MAVS [DBLP:conf/mm/FengLKZ18])
  • Video SummarizationonTvSum
    Kendall's Tau
    0.157
    best: 0.203 (DMASum)
  • Video SummarizationonTvSum
    Spearman's Rho
    0.206
    best: 0.267 (DMASum)
  • Video SummarizationonSumMe
    F1-score (Canonical)
    55.6
    best: 57.1 (PGL-SUM (maximum learning capacity))