Image To Image Translation On Gtav To

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	mIoU
class-balanced-pixel-level-self-labeling-for	60.8
g2l-a-global-to-local-alignment-method-for	59.7
context-aware-mixup-for-domain-adaptive	55.2
content-consistent-matching-for-domain	49.9
context-aware-mixup-for-domain-adaptive	70.0
advent-adversarial-entropy-minimization-for	44.8
deliberated-domain-bridging-for-domain	62.7
unsupervised-domain-adaptation-for-semantic	47.0
very-deep-convolutional-networks-for-large	41.3
sepico-semantic-guided-pixel-contrast-for	70.3
cross-region-domain-adaptation-for-class	58.6
pipa-pixel-and-patch-wise-self-supervised	71.7
deep-residual-learning-for-image-recognition	41.7
procst-boosting-semantic-segmentation-using	69.4
exploring-high-quality-target-domain	62.0
prototypical-pseudo-label-denoising-and	57.5
pipa-pixel-and-patch-wise-self-supervised	75.6
bidirectional-learning-for-domain-adaptation	41.3
hrda-context-aware-high-resolution-domain	73.8
daformer-improving-network-architectures-and	68.3
mic-masked-image-consistency-for-context	75.9
smoothing-matters-momentum-transformer-for	63.9