Image To Image Translation On Synthia To
평가 지표
mIoU (13 classes)
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
모델 이름 | mIoU (13 classes) |
---|---|
daformer-improving-network-architectures-and | 67.4 |
context-aware-mixup-for-domain-adaptive | 59.7 |
hrda-context-aware-high-resolution-domain | 72.4 |
confidence-regularized-self-training | 48.7 |
domain-adaptation-for-structured-output-via | 46.5 |
advent-adversarial-entropy-minimization-for | 48 |
instance-adaptive-self-training-for | 57.0 |
learning-to-adapt-structured-output-space-for | 46.7 |
constructing-self-motivated-pyramid | 53.3 |
a-curriculum-domain-adaptation-approach-to | 29.7 |
mic-masked-image-consistency-for-context | 74.0 |
curriculum-domain-adaptation-for-semantic | 29.0 |
classes-matter-a-fine-grained-adversarial | 52.5 |
category-anchor-guided-unsupervised-domain | 44.5 |
procst-boosting-semantic-segmentation-using | 68.2 |
smoothing-matters-momentum-transformer-for | 66.3 |
all-about-structure-adapting-structural | 41.5 |
pipa-pixel-and-patch-wise-self-supervised | 74.8 |
class-balanced-pixel-level-self-labeling-for | 65.3 |
dada-depth-aware-domain-adaptation-in | 49.8 |
sepico-semantic-guided-pixel-contrast-for | 71.4 |
context-aware-mixup-for-domain-adaptive | 69.2 |
bidirectional-learning-for-domain-adaptation | 51.4 |
sliced-wasserstein-discrepancy-for | 48.1 |
prototypical-pseudo-label-denoising-and | 62.0 |
cross-region-domain-adaptation-for-class | 63.7 |
fcns-in-the-wild-pixel-level-adversarial-and | 20.2 |
learning-to-adapt-structured-output-space-for | 45.9 |