Few Shot Semantic Segmentation On Fss 1000 5
Metriken
Mean IoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Mean IoU |
|---|---|
| hmfs-hybrid-masking-for-few-shot-segmentation | 88.5 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 88 |
| hypercorrelation-squeeze-for-few-shot | 87.8 |
| seggpt-segmenting-everything-in-context | 89.3 |
| cost-aggregation-with-4d-convolutional-swin | 90.7 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 89.9 |
| threshnet-segmentation-refinement-inspired-by | - |
| self-supervised-few-shot-learning-for-1 | 86.8 |
| hypercorrelation-squeeze-for-few-shot | 85.8 |
| doubly-deformable-aggregation-of-covariance | 91.6 |
| dense-cross-query-and-support-attention | 90.4 |
| doubly-deformable-aggregation-of-covariance | 91.7 |
| hmfs-hybrid-masking-for-few-shot-segmentation | 90.5 |
| fss-1000-a-1000-class-dataset-for-few-shot | 80.12 |
| bridge-the-points-graph-based-few-shot | 88.9 |
| cost-aggregation-is-all-you-need-for-few-shot | 90.6 |
| hypercorrelation-squeeze-for-few-shot | 88.5 |
| self-support-few-shot-semantic-segmentation | 88.6 |
| cost-aggregation-with-4d-convolutional-swin | 90.8 |
| dense-cross-query-and-support-attention | 88.8 |
| dense-cross-query-and-support-attention | 89.1 |
| self-supervised-few-shot-learning-for-1 | 87.9 |