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Models/NLDF

NLDF

Reported on 48 benchmarks across 6 tasks

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

Methodology32 results

  • 3DonDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 3DonDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 3DonSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • 3DonSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • 3DonSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • 3DonISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • 3DonUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • 3DonSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)
  • 2D ClassificationonDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 2D ClassificationonDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 2D ClassificationonSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • 2D ClassificationonSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • 2D ClassificationonSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • 2D ClassificationonISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • 2D ClassificationonUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • 2D ClassificationonSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)
  • 2D Object DetectiononDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 2D Object DetectiononDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 2D Object DetectiononSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • 2D Object DetectiononSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • 2D Object DetectiononSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • 2D Object DetectiononISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • 2D Object DetectiononUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • 2D Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)
  • 16konDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 16konDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • 16konSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • 16konSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • 16konSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • 16konISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • 16konUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • 16konSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)

Computer Vision16 results

  • Object DetectiononDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • Object DetectiononDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • Object DetectiononSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • Object DetectiononSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • Object DetectiononSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • Object DetectiononISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • Object DetectiononUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)
  • RGB Salient Object DetectiononDUTS-TE
    MAE
    0.065
    best: 0.018 (BiRefNet (DUTS, HRSOD, UHRSD))
  • RGB Salient Object DetectiononDUTS-TE
    max F-measure
    0.816
    best: 0.943 (BiRefNet (DUTS, HRSOD, UHRSD))
  • RGB Salient Object DetectiononSOC
    Average MAE
    0.106
    best: 0.089 (UCNet-CVAE)
  • RGB Salient Object DetectiononSOC
    S-Measure
    0.816
    best: 0.849 (UCNet-CVAE)
  • RGB Salient Object DetectiononSOC
    mean E-Measure
    0.837
    best: 0.872 (UCNet-CVAE)
  • RGB Salient Object DetectiononISTD
    Balanced Error Rate
    7.5
    best: 6.76 (CPD)
  • RGB Salient Object DetectiononUCF
    Balanced Error Rate
    7.69
    best: 7.21 (CPD)
  • RGB Salient Object DetectiononSBU / SBU-Refine
    Balanced Error Rate
    7.02
    best: 4.19 (CPD)