Semi Supervised Semantic Segmentation On 8
評価指標
Validation mIoU
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | Validation mIoU |
|---|---|
| unimatch-v2-pushing-the-limit-of-semi | 85.1% |
| n-cps-generalising-cross-pseudo-supervision | 79.29% |
| switching-temporary-teachers-for-semi | 80.52 |
| semi-supervised-semantic-segmentation-with-5 | 75.33% |
| perturbed-and-strict-mean-teachers-for-semi | 79.22% |
| adversarial-learning-for-semi-supervised | 65.70% |
| classmix-segmentation-based-data-augmentation | 66.29% |
| lasermix-for-semi-supervised-lidar-semantic | 79.1% |
| semi-supervised-semantic-segmentation-via-2 | 80.28% |
| revisiting-weak-to-strong-consistency-in-semi | 79.5% |
| bootstrapping-semantic-segmentation-with | 68.69% |
| semivl-semi-supervised-semantic-segmentation | 80.6% |
| semi-supervised-semantic-segmentation-using-2 | 79.12% |
| semi-supervised-semantic-segmentation-with-6 | 79.11% |
| guidedmix-net-learning-to-improve-pseudo | 69.8% |
| a-simple-baseline-for-semi-supervised | 78.7% |
| revisiting-and-maximizing-temporal-knowledge | 79.2% |
| corrmatch-label-propagation-via-correlation | 80.4% |
| semi-supervised-semantic-segmentation-via-3 | 79.76% |
| revisiting-and-maximizing-temporal-knowledge | 80.1% |
| conservative-progressive-collaborative | 78.17% |
| semi-supervised-semantic-segmentation-with-3 | 80.21% |