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Models/BMPM

BMPM

Reported on 30 benchmarks across 6 tasks

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

Methodology20 results

  • 3DonPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • 3DonISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • 3DonUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • 3DonSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • 3DonSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)
  • 2D ClassificationonPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • 2D ClassificationonISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • 2D ClassificationonUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • 2D ClassificationonSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • 2D ClassificationonSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)
  • 2D Object DetectiononPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • 2D Object DetectiononISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • 2D Object DetectiononUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • 2D Object DetectiononSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • 2D Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)
  • 16konPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • 16konISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • 16konUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • 16konSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • 16konSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)

Computer Vision10 results

  • Object DetectiononPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • Object DetectiononISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • Object DetectiononUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • Object DetectiononSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)
  • RGB Salient Object DetectiononPASCAL-S
    MAE
    0.074
    best: 0.039 (CFDN)
  • RGB Salient Object DetectiononISTD
    Balanced Error Rate
    7.1
    best: 6.76 (CPD)
  • RGB Salient Object DetectiononUCF
    Balanced Error Rate
    8.09
    best: 7.21 (CPD)
  • RGB Salient Object DetectiononSOD
    MAE
    0.108
    best: 0.102 (PoolNet (VGG-16))
  • RGB Salient Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    6.17
    best: 4.19 (CPD)