Semi Supervised Semantic Segmentation On 1
Metriken
Validation mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Validation mIoU |
|---|---|
| a-simple-baseline-for-semi-supervised | 77.8% |
| semi-supervised-semantic-segmentation-using-2 | 78.51% |
| semi-supervised-semantic-segmentation-via-3 | 79.52% |
| bootstrapping-semantic-segmentation-with | 68.50% |
| semi-supervised-semantic-segmentation-with-3 | 79.21% |
| unimatch-v2-pushing-the-limit-of-semi | 84.5% |
| perturbed-and-strict-mean-teachers-for-semi | 78.38% |
| revisiting-and-maximizing-temporal-knowledge | 78.8% |
| lasermix-for-semi-supervised-lidar-semantic | 78.3% |
| semi-supervised-semantic-segmentation-with-5 | 73.52% |
| guidedmix-net-learning-to-improve-pseudo | 67.5% |
| semivl-semi-supervised-semantic-segmentation | 80.3% |
| adversarial-learning-for-semi-supervised | 60.5% |
| confidence-weighted-boundary-aware-learning | 78.43% |
| switching-temporary-teachers-for-semi | 79.46 |
| the-gist-and-rist-of-iterative-self-training | 65.14% |
| bootstrapping-semantic-segmentation-with | 67.53% |
| three-ways-to-improve-semantic-segmentation | 69.38% |
| corrmatch-label-propagation-via-correlation | 79.4% |
| consistency-regularization-and-cutmix-for | 63.87% |
| semi-supervised-semantic-segmentation-with-6 | 78.4% |
| semi-supervised-semantic-segmentation-with-2 | 65.9% |
| classmix-segmentation-based-data-augmentation | 63.63% |
| semi-supervised-semantic-segmentation-with | 61.9% |
| semi-supervised-semantic-segmentation-via-2 | 79.01% |
| conservative-progressive-collaborative | 76.98% |
| n-cps-generalising-cross-pseudo-supervision | 78.41% |
| revisiting-and-maximizing-temporal-knowledge | 80.1% |
| revisiting-weak-to-strong-consistency-in-semi | 79.22% |