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Models/ConstellationNets

ConstellationNets

Reported on 12 benchmarks across 2 tasks

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

Computer Vision12 results

  • Image ClassificationonCIFAR-FS 5-way (1-shot)
    Accuracy
    75.4
    best: 89.94 (PT+MAP+SF+SOT (transductive))
  • Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy
    79.95
    best: 98.72 (SgVA-CLIP)
  • Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy
    64.89
    best: 97.95 (SgVA-CLIP)
  • Image ClassificationonFC100 5-way (5-shot)
    Accuracy
    59.7
    best: 70.6 (BAVARDAGE)
  • Image ClassificationonFC100 5-way (1-shot)
    Accuracy
    43.8
    best: 57.27 (BAVARDAGE)
  • Image ClassificationonCIFAR-FS 5-way (5-shot)
    Accuracy
    86.8
    best: 93.5 (CAML [Laion-2b])
  • Few-Shot Image ClassificationonCIFAR-FS 5-way (1-shot)
    Accuracy
    75.4
    best: 89.94 (PT+MAP+SF+SOT (transductive))
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy
    79.95
    best: 98.72 (SgVA-CLIP)
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy
    64.89
    best: 97.95 (SgVA-CLIP)
  • Few-Shot Image ClassificationonFC100 5-way (5-shot)
    Accuracy
    59.7
    best: 70.6 (BAVARDAGE)
  • Few-Shot Image ClassificationonFC100 5-way (1-shot)
    Accuracy
    43.8
    best: 57.27 (BAVARDAGE)
  • Few-Shot Image ClassificationonCIFAR-FS 5-way (5-shot)
    Accuracy
    86.8
    best: 93.5 (CAML [Laion-2b])