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Models/SL (B2)

SL (B2)

Reported on 6 benchmarks across 6 tasks · 1 paper

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

Computer Vision3 results

  • Face ReconstructiononAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421
  • Facial Expression Recognition (FER)onAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421
  • 3D Face ReconstructiononAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421

Music1 result

  • Facial Recognition and ModellingonAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421

Methodology1 result

  • 3DonAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421

Medical1 result

  • 3D Face ModellingonAffectNet
    Accuracy (8 emotion)· uses extra data· 2021-05-13
    60.35
    best: 68.69 (Norface)
    Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationarXiv:2105.06421