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Semantic Image Synthesis on Cityscapes (val)

76.3mIoUDP-SIMS
46.03653.89361.7569.607Dec 8, 2020Jun 10, 2021Dec 12, 2021Jun 14, 2022Dec 16, 2022Jun 18, 2023Dec 20, 2023

Evaluation results

MethodLinks
DP-SIMSBackbone=ConvNeXt-L2023.1276.3—38.278.5
DP-GAN2023.1273.6———
OASISNumber of parameters=94M, VGG Perceptual Loss=false2020.1269.3—47.7—
OASIS2023.1269.3—47.772
SDM2023.1269.1—42.172.8
LGGANNumber of parameters=n/a, VGG Perceptual Loss=true2020.1268.4—57.7—
CC-FPSENumber of parameters=131M, VGG Perceptual Loss=true2020.1265.5—54.3—
SPADE+Number of parameters=102M, VGG Perceptual Loss=true2020.1264—47.8—
SPADENumber of parameters=102M, VGG Perceptual Loss=true2020.1262.3—71.8—
SPADE2023.1262.3—71.8—
Pix2pixHDNumber of parameters=183M, VGG Perceptual Loss=true2020.1258.3—95—
CRNNumber of parameters=84M, VGG Perceptual Loss=true2020.1252.4—104.7—
Pix2pixHD2023.1252.4—104.7—
SPADE+Number of parameters=102M, VGG Perceptual Loss=false2020.1247.6—61.4—
SIMSNumber of parameters=56M2020.1247.2—49.7—
Cascaded Refinement NetworkBaseline compared against=Image-space loss2017.07—99.7——
Cascaded Refinement NetworkBaseline compared against=GAN+SemSeg2017.07—98.5——
Cascaded Refinement NetworkBaseline compared against=Isola et al. [16]2017.07—96.9——
Cascaded Refinement NetworkBaseline compared against=Encoder-decoder2017.07—78.3——
Cascaded Refinement NetworkBaseline compared against=Full-resolution network2017.07—67.7——