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Models/Semantic-Aware Scene Recognition (ResNet-50)

Semantic-Aware Scene Recognition (ResNet-50)

Reported on 8 benchmarks across 4 tasks · 1 paper

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

Computer Vision6 results

  • Scene ParsingonMIT Indoor Scenes
    Accuracy· 2019-09-05
    87.1
    best: 90.3 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • Scene ParsingonSUN397
    Accuracy· 2019-09-05
    74.04
    best: 77.28 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • AnimationonMIT Indoor Scenes
    Accuracy· 2019-09-05
    87.1
    best: 90.3 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • AnimationonSUN397
    Accuracy· 2019-09-05
    74.04
    best: 77.28 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • 3D Character Animation From A Single PhotoonMIT Indoor Scenes
    Accuracy· 2019-09-05
    87.1
    best: 90.3 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • 3D Character Animation From A Single PhotoonSUN397
    Accuracy· 2019-09-05
    74.04
    best: 77.28 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410

Audio2 results

  • 2D Semantic SegmentationonMIT Indoor Scenes
    Accuracy· 2019-09-05
    87.1
    best: 90.3 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410
  • 2D Semantic SegmentationonSUN397
    Accuracy· 2019-09-05
    74.04
    best: 77.28 (FOSNet)
    Semantic-Aware Scene RecognitionarXiv:1909.02410