Domain Adaptation On Gta5 To Cityscapes
Metriken
mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | mIoU |
|---|---|
| pipa-pixel-and-patch-wise-self-supervised | 75.6 |
| daformer-improving-network-architectures-and | 68.3 |
| prototypical-pseudo-label-denoising-and | 57.5 |
| smoothing-matters-momentum-transformer-for | 63.9 |
| fredom-fairness-domain-adaptation-approach-to | 73.6 |
| iterative-loop-learning-combining-self | 76.1 |
| mic-masked-image-consistency-for-context | 75.9 |
| a-novel-unsupervised-domain-adaption-method | 58.8 |
| sepico-semantic-guided-pixel-contrast-for | 70.3 |
| hyperbolic-active-learning-for-semantic | 77.8 |
| cross-region-domain-adaptation-for-class | 58.6 |
| fredom-fairness-domain-adaptation-approach-to | 61.3 |
| transferring-to-real-world-layouts-a-depth | 77.7 |
| generalize-then-adapt-source-free-domain | 53.4 |
| procst-boosting-semantic-segmentation-using | 69.4 |
| bidirectional-self-training-with-multiple | 61.2 |
| g2l-a-global-to-local-alignment-method-for | 59.7 |
| unsupervised-scene-adaptation-with-memory | 48.3 |
| exploring-high-quality-target-domain | 62.0 |
| rectifying-pseudo-label-learning-via | 50.3 |
| hrda-context-aware-high-resolution-domain | 73.8 |
| improve-cross-domain-mixed-sampling-with | 67.0 |
| deliberated-domain-bridging-for-domain | 62.7 |
| prototypical-contrast-adaptation-for-domain | 56.3 |
| content-consistent-matching-for-domain | 49.9 |
| adaptive-boosting-for-domain-adaptation | 49.0 |