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Models/DRLN-BAM

DRLN-BAM

Reported on 8 benchmarks across 4 tasks · 1 paper · 4 SOTA

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

Computer Vision4 results

  • Image Super-ResolutiononSet14 - 4x upscaling
    PSNR· 2021-04-15
    29.08
    best: 29.54 (DRCT-L)
    SOTA
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566
  • 3D Object Super-ResolutiononSet14 - 4x upscaling
    PSNR· 2021-04-15
    29.08
    best: 29.54 (DRCT-L)
    SOTA
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566
  • Image Super-ResolutiononSet14 - 4x upscaling
    SSIM· 2021-04-15
    0.7925
    best: 0.894 (Edge-informed SR)
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566
  • 3D Object Super-ResolutiononSet14 - 4x upscaling
    SSIM· 2021-04-15
    0.7925
    best: 0.894 (Edge-informed SR)
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566

Graphs2 results

  • Super-ResolutiononSet14 - 4x upscaling
    PSNR· 2021-04-15
    29.08
    best: 29.54 (DRCT-L)
    SOTA
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566
  • Super-ResolutiononSet14 - 4x upscaling
    SSIM· 2021-04-15
    0.7925
    best: 0.894 (Edge-informed SR)
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566

Methodology2 results

  • 16konSet14 - 4x upscaling
    PSNR· 2021-04-15
    29.08
    best: 29.54 (DRCT-L)
    SOTA
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566
  • 16konSet14 - 4x upscaling
    SSIM· 2021-04-15
    0.7925
    best: 0.894 (Edge-informed SR)
    BAM: A Balanced Attention Mechanism for Single Image Super ResolutionarXiv:2104.07566