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Models/Illumination Augmentation

Illumination Augmentation

Reported on 12 benchmarks across 2 tasks · 1 paper · 10 SOTA

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

Computer Vision12 results

  • Image ClassificationonCUB 200 5-way 5-shot
    Accuracy· uses extra data· 2021-02-06
    96.28
    best: 98.7 (CAML [Laion-2b])
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Image ClassificationonCUB 200 5-way 1-shot
    Accuracy· 2021-02-06
    94.73
    best: 95.8 (PT+MAP+SF+SOT (transductive))
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Image ClassificationonCIFAR-FS 5-way (1-shot)
    Accuracy· 2021-02-06
    87.73
    best: 89.94 (PT+MAP+SF+SOT (transductive))
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy· 2021-02-06
    82.99
    best: 97.95 (SgVA-CLIP)
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Image ClassificationonCIFAR-FS 5-way (5-shot)
    Accuracy· 2021-02-06
    91.09
    best: 93.5 (CAML [Laion-2b])
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonCUB 200 5-way 5-shot
    Accuracy· uses extra data· 2021-02-06
    96.28
    best: 98.7 (CAML [Laion-2b])
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonCUB 200 5-way 1-shot
    Accuracy· 2021-02-06
    94.73
    best: 95.8 (PT+MAP+SF+SOT (transductive))
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonCIFAR-FS 5-way (1-shot)
    Accuracy· 2021-02-06
    87.73
    best: 89.94 (PT+MAP+SF+SOT (transductive))
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy· 2021-02-06
    82.99
    best: 97.95 (SgVA-CLIP)
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonCIFAR-FS 5-way (5-shot)
    Accuracy· 2021-02-06
    91.09
    best: 93.5 (CAML [Laion-2b])
    SOTA
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy· 2021-02-06
    89.14
    best: 98.72 (SgVA-CLIP)
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy· 2021-02-06
    89.14
    best: 98.72 (SgVA-CLIP)
    Sill-Net: Feature Augmentation with Separated Illumination RepresentationarXiv:2102.03539