Unsupervised Domain Adaptation On Gtav To
Metrics
mIoU
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | mIoU |
|---|---|
| procst-boosting-semantic-segmentation-using | 69.4 |
| bidirectional-self-training-with-multiple | 61.2 |
| sepico-semantic-guided-pixel-contrast-for | 70.3 |
| pipa-pixel-and-patch-wise-self-supervised | 75.6 |
| smoothing-matters-momentum-transformer-for | 63.9 |
| context-aware-mixup-for-domain-adaptive | 55.2 |
| dual-level-interaction-for-domain-adaptive | 71.0 |
| bimal-bijective-maximum-likelihood-approach | 47.3 |
| daformer-improving-network-architectures-and | 68.3 |
| rectifying-pseudo-label-learning-via | 50.3 |
| pipa-pixel-and-patch-wise-self-supervised | 71.7 |
| context-aware-mixup-for-domain-adaptive | 70.0 |
| unsupervised-scene-adaptation-with-memory | 45.5 |
| g2l-a-global-to-local-alignment-method-for | 59.7 |
| a-novel-unsupervised-domain-adaption-method | 58.8 |
| mic-masked-image-consistency-for-context | 75.9 |
| adaptive-boosting-for-domain-adaptation | 50.9 |
| rethinking-ensemble-distillation-for-semantic | 57.98 |
| hrda-context-aware-high-resolution-domain | 73.8 |
| cluda-contrastive-learning-in-unsupervised | 74.4 |