Semantic Segmentation on NYU-depth v2 (IoU and Pixel Accuracy)
58.8mIoURSGMamba-B
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| RSGMamba-BBackbone=SegMAN-B, Params.=48.6M, Input size=480 × 640, Flops=130.2G2026.04 | 58.8 | — | — | |
| DFormerV2Backbone=DFormerV2-L, Params.=95.5M, Input size=480 × 640, Flops=124.1G2026.04 | 58.4 | — | — | |
| DFNetBackbone=MiT-B4, Params.=108.8M, Input size=480 × 640, Flops=101.0G2026.04 | 57.9 | — | — | |
| PrimKDBackbone=MiT-B4, Params.=139.9M, Input size=480 × 640, Flops=134.3G2026.04 | 57.8 | — | — | |
| ADBNetBackbone=ConvNext-base, Params.=299.1M, Input size=480 × 640, Flops=361.7G2026.04 | 57.6 | — | — | |
| SigmaBackbone=VMamba-S, Params.=69.8M, Input size=480 × 640, Flops=139.8G2026.04 | 57 | — | — | |
| CMXBackbone=MiT-B5, Params.=181.1M, Input size=480 × 640, Flops=167.8G2026.04 | 56.9 | — | — | |
| CMNextBackbone=MiT-B4, Params.=119.6M, Input size=480 × 640, Flops=131.9G2026.04 | 56.9 | — | — | |
| GeminiFusionBackbone=MiT-B3, Params.=75.8M, Input size=480 × 640, Flops=174.0G2026.04 | 56.8 | — | — | |
| RSGMamba-SBackbone=SegMAN-S, Params.=28.1M, Input size=480 × 640, Flops=58.5G2026.04 | 56.4 | — | — | |
| CMXBackbone=MiT-B4, Params.=139.9M, Input size=480 × 640, Flops=134.3G2026.04 | 56.3 | — | — | |
| DiffPixelFormerBackbone=MiT-B3, Params.=85.4M, Input size=480 × 640, Flops=154.8G2026.04 | 56.3 | — | — | |
| DFNetBackbone=MiT-B2, Params.=41.2M, Input size=480 × 640, Flops=35.1G2026.04 | 56.1 | — | — | |
| ADBNetBackbone=ConvNext-tiny, Params.=45.9M, Input size=480 × 640, Flops=52.7G2026.04 | 56 | — | — | |
| ECMRNBackbone=DFormer, Params.=68.6M, Input size=480 × 640, Flops=135.3G2026.04 | 54.6 | — | — | |
| TokenFusionBackbone=MiT-B3, Params.=45.9M, Input size=480 × 640, Flops=94.4G2026.04 | 54.2 | — | — | |
| SigmaBackbone=VMamba-T, Params.=48.3M, Input size=480 × 640, Flops=90.4G2026.04 | 53.9 | — | — | |
| DCANetBackbone=VMamba, Params.=123.8M, Input size=480 × 640, Flops=138.5G2026.04 | 53.3 | — | — | |
| RefineNet-LW-101Data=RGB2021.07 | 43.6 | — | — | |
| TD2-PSP50Data=RGB2021.07 | 43.5 | — | — | |
| CI-NetData=RGB2021.07 | 42.6 | — | 72.7 | |
| 3DGNNData=RGBD2021.07 | 42 | — | — | |
| D-CNNData=RGBD2021.07 | 41 | — | — | |
| ContextData=RGB2021.07 | 40.6 | — | 70 | |
| Eigen et al.Data=RGB2021.07 | 34.1 | — | 65.6 | |
| B-SegNetData=RGB2021.07 | 32.4 | — | 68 | |
| Deng et al.Data=RGBD2021.07 | 31.5 | — | 63.8 | |
| FCNData=RGB2021.07 | 29.2 | — | 60 | |
| baselineBackbone=ResNet-502017.05 | — | 40.6 | 70.3 | |
| Context2017.05 | — | 40.6 | 70 | |
| Cross-stitch networkBackbone=VGG-162018.01 | — | 34.8 | 65 | |
| Cross-stitch networkBackbone=VGG-16-Shortcut2018.01 | — | 35 | 65.1 | |
| loop1 w/ gt-depthBackbone=ResNet-50, Number of loops=1, Depth Usage=ground-truth depth2017.05 | — | 42.5 | 71.1 | |
| loop1 w/ pred-depthBackbone=ResNet-50, Number of loops=1, Depth Usage=predicted depth2017.05 | — | 42.7 | 71.2 | |
| loop1 w/o depthBackbone=ResNet-50, Number of loops=1, Depth Usage=none2017.05 | — | 41.9 | 70.6 | |
| loop2Backbone=ResNet-50, Number of loops=2, Depth Usage=predicted depth2017.05 | — | 43.1 | 71.3 | |
| loop2 (test-aug)Backbone=ResNet-50, Number of loops=2, Depth Usage=predicted depth, test-time augmentation=true2017.05 | — | 44.5 | 72.1 | |
| Multi-task baselineBackbone=VGG-16-Shortcut2018.01 | — | 33.6 | 64.4 | |
| Multiple-task baselineBackbone=VGG-162018.01 | — | 33.4 | 64.2 | |
| NDDR-CNNBackbone=VGG-162018.01 | — | 36.2 | 66.4 | |
| NDDR-CNN-ShortcutBackbone=VGG-16-Shortcut2018.01 | — | 36.7 | 67 | |
| RefineNet-Res101Backbone=ResNet-1012017.05 | — | 44.7 | — | |
| RefineNet-Res152Backbone=ResNet-1522017.05 | — | 46.5 | 73.6 | |
| RefineNet-Res50Backbone=ResNet-502017.05 | — | 43.8 | — | |
| Single-task baselineBackbone=VGG-162018.01 | — | 33.5 | 64.1 | |
| Single-task baselineBackbone=VGG-16-Shortcut2018.01 | — | 33.5 | 64.4 | |
| Sluice networkBackbone=VGG-162018.01 | — | 34.9 | 65.2 | |
| Sluice networkBackbone=VGG-16-Shortcut2018.01 | — | 35.3 | 65.3 | |
| w/ gt-depthBackbone=ResNet-50, Depth Usage=ground-truth depth2017.05 | — | 41.3 | 70.8 | |
| w/ pred-depthBackbone=ResNet-50, Depth Usage=predicted depth2017.05 | — | 41.8 | 71.1 |