Synthetic To Real Translation On Gtav To
المقاييس
mIoU
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | mIoU |
|---|---|
| sepico-semantic-guided-pixel-contrast-for | 70.3 |
| light-weight-calibrator-a-separable-component | 40.5 |
| sepico-semantic-guided-pixel-contrast-for | 61.0 |
| curriculum-domain-adaptation-for-semantic | 28.9 |
| a-curriculum-domain-adaptation-approach-to | 31.4 |
| mlsl-multi-level-self-supervised-learning-for | 49 |
| cluda-contrastive-learning-in-unsupervised | 74.4 |
| deliberated-domain-bridging-for-domain | 62.7 |
| class-balanced-pixel-level-self-labeling-for | 60.8 |
| g2l-a-global-to-local-alignment-method-for | 59.7 |
| daformer-improving-network-architectures-and | 68.3 |
| domain-adaptation-for-structured-output-via | 46.5 |
| taking-a-closer-look-at-domain-shift-category | 43.2 |
| bidirectional-self-training-with-multiple | 61.2 |
| advent-adversarial-entropy-minimization-for | 45.5 |
| self-supervised-augmentation-consistency-for | 53.8 |
| cycada-cycle-consistent-adversarial-domain | 34.8 |
| permuted-adain-enhancing-the-representation | 51.5 |
| multiple-fusion-adaptation-a-strong-framework | 58.2 |
| hrda-context-aware-high-resolution-domain | 73.8 |
| domain-adaptation-for-semantic-segmentation | 47.0 |
| switchable-whitening-for-deep-representation | 35.7 |
| unsupervised-domain-adaptation-through-self-1 | 41.2 |
| exploiting-image-translations-via-ensemble | 51.66 |
| spcl-a-new-framework-for-domain-adaptive | 52.1 |
| pixel-level-intra-domain-adaptation-for | 54.2 |
| dsp-dual-soft-paste-for-unsupervised-domain | 55.0 |
| smoothing-matters-momentum-transformer-for | 63.9 |
| exploring-high-quality-target-domain | 58.8 |
| all-about-structure-adapting-structural | 45.4 |
| instance-adaptive-self-training-for | 51.5 |
| rectifying-pseudo-label-learning-via | 50.3 |
| diverse-image-to-image-translation-via | 43.2 |
| context-aware-mixup-for-domain-adaptive | 55.2 |
| classes-matter-a-fine-grained-adversarial | 50.1 |
| sliced-wasserstein-discrepancy-for | 44.5 |
| metacorrection-domain-aware-meta-loss | 52.1 |
| unsupervised-domain-adaptation-for-semantic-2 | 52.8 |
| pixel-level-cycle-association-a-new | 47.8 |
| pipa-pixel-and-patch-wise-self-supervised | 75.6 |
| unsupervised-scene-adaptation-with-memory | 48.3 |
| prototypical-pseudo-label-denoising-and | 57.5 |
| dacs-domain-adaptation-via-cross-domain-mixed | 52.14 |
| unsupervised-intra-domain-adaptation-for | 46.3 |
| pixmatch-unsupervised-domain-adaptation-via | 50.3 |
| learning-content-enhanced-mask-transformer | 59.7 |
| road-reality-oriented-adaptation-for-semantic | 39.4 |
| confidence-regularized-self-training | 49.8 |
| mic-masked-image-consistency-for-context | 75.9 |
| cross-region-domain-adaptation-for-class | 58.6 |
| category-anchor-guided-unsupervised-domain | 50.2 |
| learning-to-adapt-structured-output-space-for | 42.4 |
| adaptive-boosting-for-domain-adaptation | 50.9 |
| pipa-pixel-and-patch-wise-self-supervised | 71.7 |
| cycada-cycle-consistent-adversarial-domain | 39.5 |
| context-aware-mixup-for-domain-adaptive | 70.0 |
| tridentadapt-learning-domain-invariance-via | 53.3 |
| elda-using-edges-to-have-an-edge-on-semantic | 57.3 |
| contrastive-learning-and-self-training-for | 51.6 |
| coarse-to-fine-domain-adaptive-semantic | 56.1 |
| transferring-to-real-world-layouts-a-depth | 77.7 |
| fcns-in-the-wild-pixel-level-adversarial-and | 27.1 |
| domain-adaptive-semantic-segmentation-with | 56.6 |
| bidirectional-learning-for-domain-adaptation | 48.5 |
| cluda-contrastive-learning-in-unsupervised | 70.11 |
| constructing-self-motivated-pyramid | 48 |
| domain-adaptive-semantic-segmentation-with-1 | 53.5 |
| content-consistent-matching-for-domain | 49.9 |
| exploring-high-quality-target-domain | 62.0 |
| procst-boosting-semantic-segmentation-using | 69.4 |
| dual-path-learning-for-domain-adaptation-of | 53.3 |