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Models/FSS-1000 (VGG-16)

FSS-1000 (VGG-16)

Reported on 6 benchmarks across 3 tasks · 1 paper · 6 SOTA

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· 2019-07-29
80.12
best: 91.7 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347
Few-Shot LearningonFSS-1000 (1-shot)
Mean IoU· 2019-07-29
73.47
best: 90.8 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347
Meta-LearningonFSS-1000 (5-shot)
Mean IoU· 2019-07-29
80.12
best: 91.7 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347
Meta-LearningonFSS-1000 (1-shot)
Mean IoU· 2019-07-29
73.47
best: 90.8 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347

Computer Vision2 results

Few-Shot Semantic SegmentationonFSS-1000 (5-shot)
Mean IoU· 2019-07-29
80.12
best: 91.7 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347
Few-Shot Semantic SegmentationonFSS-1000 (1-shot)
Mean IoU· 2019-07-29
73.47
best: 90.8 (DACM (ResNet-101))
SOTA
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation arXiv:1907.12347