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Models/DACL (ResNet-18)

DACL (ResNet-18)

Reported on 12 benchmarks across 6 tasks

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

Computer Vision6 results

  • Face ReconstructiononRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • Face ReconstructiononRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)
  • Facial Expression Recognition (FER)onRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • Facial Expression Recognition (FER)onRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)
  • 3D Face ReconstructiononRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • 3D Face ReconstructiononRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)

Music2 results

  • Facial Recognition and ModellingonRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • Facial Recognition and ModellingonRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)

Methodology2 results

  • 3DonRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • 3DonRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)

Medical2 results

  • 3D Face ModellingonRAF-DB
    Avg. Accuracy· uses extra data
    80.44
    best: 87.5 (C-EXPR-NET)
  • 3D Face ModellingonRAF-DB
    Overall Accuracy· uses extra data
    87.78
    best: 94.76 (ResEmoteNet)