Semi Supervised Semantic Segmentation On 2
평가 지표
Validation mIoU
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Validation mIoU |
|---|---|
| semi-supervised-semantic-segmentation-via | 63.03% |
| unimatch-v2-pushing-the-limit-of-semi | 84.3% |
| revisiting-and-maximizing-temporal-knowledge | 78.9% |
| revisiting-weak-to-strong-consistency-in-semi | 77.92% |
| semi-supervised-semantic-segmentation-using-2 | 76.48% |
| dense-fixmatch-a-simple-semi-supervised | 73.91% |
| semi-supervised-semantic-segmentation-with-5 | 70.33% |
| semi-supervised-semantic-segmentation-via-2 | 77.9% |
| a-simple-baseline-for-semi-supervised | 74.1% |
| consistency-regularization-and-cutmix-for | 60.34% |
| confidence-weighted-boundary-aware-learning | 77.20% |
| perturbed-and-strict-mean-teachers-for-semi | 77.12% |
| lasermix-for-semi-supervised-lidar-semantic | 77.1% |
| the-gist-and-rist-of-iterative-self-training | 62.57% |
| semi-supervised-semantic-segmentation-with-3 | 77.62% |
| classmix-segmentation-based-data-augmentation | 61.35% |
| dense-fixmatch-a-simple-semi-supervised | 73.39% |
| semivl-semi-supervised-semantic-segmentation | 79.4% |
| conservative-progressive-collaborative | 74.6% |
| semi-supervised-semantic-segmentation-with | 59.3% |
| switching-temporary-teachers-for-semi | 78.4 |
| semi-supervised-semantic-segmentation-via-3 | 77.78% |
| adversarial-learning-for-semi-supervised | 57.1% |
| semi-supervised-semantic-segmentation-with-6 | 76.31% |
| bootstrapping-semantic-segmentation-with | 64.94% |
| bootstrapping-semantic-segmentation-with | 66.44% |
| revisiting-and-maximizing-temporal-knowledge | 77.8% |
| corrmatch-label-propagation-via-correlation | 78.5% |
| three-ways-to-improve-semantic-segmentation | 68.01% |
| guidedmix-net-learning-to-improve-pseudo | 65.8% |
| n-cps-generalising-cross-pseudo-supervision | 77.61% |
| semi-supervised-semantic-segmentation-with-2 | 64.4% |