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Models/Annotation-free FSS (Without Annotation,ResNet-50)

Annotation-free FSS (Without Annotation,ResNet-50)

Reported on 6 benchmarks across 3 tasks · 1 paper

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

Methodology4 results

  • Few-Shot LearningonFSS-1000 (5-shot)
    Mean IoU· 2023-07-26
    86.8
    best: 91.7 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446
  • Few-Shot LearningonFSS-1000 (1-shot)
    Mean IoU· 2023-07-26
    85
    best: 90.8 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446
  • Meta-LearningonFSS-1000 (5-shot)
    Mean IoU· 2023-07-26
    86.8
    best: 91.7 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446
  • Meta-LearningonFSS-1000 (1-shot)
    Mean IoU· 2023-07-26
    85
    best: 90.8 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446

Computer Vision2 results

  • Few-Shot Semantic SegmentationonFSS-1000 (5-shot)
    Mean IoU· 2023-07-26
    86.8
    best: 91.7 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446
  • Few-Shot Semantic SegmentationonFSS-1000 (1-shot)
    Mean IoU· 2023-07-26
    85
    best: 90.8 (DACM (ResNet-101))
    Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproacharXiv:2307.14446