task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DenseMTL | https://arxiv.org/abs/2206.08927v2 | Mean IoU | 40.84% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | STD2P | http://arxiv.org/abs/1604.02388v3 | Mean IoU | 40.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MaskSup | https://arxiv.org/abs/2210.00923v2 | Mean IoU | 39.31% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HeMIS | http://arxiv.org/abs/1607.05194v1 | Mean IoU | 37.77% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TD4-PSP18 | https://arxiv.org/abs/2004.01800v2 | Mean IoU | 37.4 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Bayesian DenseNet | http://arxiv.org/abs/1703.04977v2 | Mean IoU | 37.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HN-network | https://arxiv.org/abs/2002.02200v1 | Mean IoU | 33.49% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CompL | https://arxiv.org/abs/2210.07239v1 | Mean IoU | 33.48% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Dilated FCN-2s RGB | http://arxiv.org/abs/1707.08254v3 | Mean IoU | 32.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | AdaShare | https://arxiv.org/abs/1911.12423v2 | Mean IoU | 29.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | EDNAS+JAReD | https://arxiv.org/abs/2210.01384v1 | Mean IoU | 22.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Cross-stitch | http://arxiv.org/abs/1604.03539v1 | Mean IoU | 19.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | FCN-32s RGB-HHA | http://arxiv.org/abs/1605.06211v1 | Mean Accuracy | 44 |
10-shot image generation > Semantic Segmentation | Potsdam | HRNet-48 | https://arxiv.org/abs/1908.07919v2 | mIoU | 84.22 |
10-shot image generation > Semantic Segmentation | Potsdam | HRNet-18 | https://arxiv.org/abs/1908.07919v2 | mIoU | 84.02 |
10-shot image generation > Semantic Segmentation | Potsdam | DeepLabV3+ | http://arxiv.org/abs/1802.02611v3 | mIoU | 83.67 |
10-shot image generation > Semantic Segmentation | BIG | PSPNet + CascadePSP | https://arxiv.org/abs/2005.02551v1 | IoU | 93.93 |
10-shot image generation > Semantic Segmentation | BIG | PSPNet + CascadePSP | https://arxiv.org/abs/2005.02551v1 | mBA | 75.32 |
10-shot image generation > Semantic Segmentation | BIG | RefineNet + CascadePSP | https://arxiv.org/abs/2005.02551v1 | IoU | 92.79 |
10-shot image generation > Semantic Segmentation | BIG | RefineNet + CascadePSP | https://arxiv.org/abs/2005.02551v1 | mBA | 74.77 |
10-shot image generation > Semantic Segmentation | BIG | DeepLabV3+ + CascadePSP | https://arxiv.org/abs/2005.02551v1 | IoU | 92.23 |
10-shot image generation > Semantic Segmentation | BIG | DeepLabV3+ + CascadePSP | https://arxiv.org/abs/2005.02551v1 | mBA | 74.59 |
10-shot image generation > Semantic Segmentation | BIG | FCN + CascadePSP | https://arxiv.org/abs/2005.02551v1 | IoU | 77.87 |
10-shot image generation > Semantic Segmentation | BIG | FCN + CascadePSP | https://arxiv.org/abs/2005.02551v1 | mBA | 67.04 |
10-shot image generation > Semantic Segmentation | ApolloScape | ERFNet-IntRA-KD (ours) | https://arxiv.org/abs/2004.05304v1 | mIoU | 43.2 |
10-shot image generation > Semantic Segmentation | ApolloScape | deeplabv3 | https://arxiv.org/abs/2311.18741v3 | mIoU | 0.43 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐ResNet‐101 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | Acc | 90.78 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐ResNet‐101 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | mIoU | 65.88 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐Xception‐65 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | Acc | 90.72 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐Xception‐65 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | mIoU | 64.34 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐ResNet‐50 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | Acc | 78.65 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐ResNet‐50 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | mIoU | 43.65 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐Xception‐71 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | Acc | 74.31 |
10-shot image generation > Semantic Segmentation | Okutama Drone and Swiss Drone Dataset | DeepLabv3+‐Xception‐71 | https://onlinelibrary.wiley.com/doi/full/10.1002/rob.22082 | mIoU | 37.81 |
10-shot image generation > Semantic Segmentation | VDD | Segformer-B2 | https://arxiv.org/abs/2305.13608v4 | mIoU | 85.75 |
10-shot image generation > Semantic Segmentation | VDD | UperNet(Swin-L) | https://arxiv.org/abs/2305.13608v4 | mIoU | 85.63 |
10-shot image generation > Semantic Segmentation | VDD | UperNet(Swin-T) | https://arxiv.org/abs/2305.13608v4 | mIoU | 84.73 |
10-shot image generation > Semantic Segmentation | VDD | Mask2Former(ResNet-50) | https://arxiv.org/abs/2305.13608v4 | mIoU | 83.21 |
10-shot image generation > Semantic Segmentation | VDD | Segformer-B5 | https://arxiv.org/abs/2305.13608v4 | mIoU | 82.11 |
10-shot image generation > Semantic Segmentation | VDD | Mask2Former(Swin-T) | https://arxiv.org/abs/2305.13608v4 | mIoU | 77.85 |
10-shot image generation > Semantic Segmentation | VDD | Segformer-B0 | https://arxiv.org/abs/2305.13608v4 | mIoU | 75.37 |
10-shot image generation > Semantic Segmentation | HAM10000 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Average Dice | 90.6 |
10-shot image generation > Semantic Segmentation | HAM10000 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Average IOU | 90.2 |
10-shot image generation > Semantic Segmentation | Fine-Grained Cloud Segmentation Dataset | D2LS | https://arxiv.org/abs/2503.06683v1 | mIoU | 82.16 |
10-shot image generation > Semantic Segmentation | Fine-Grained Cloud Segmentation Dataset | SFA-Net | https://www.mdpi.com/2072-4292/16/17/3278 | mIoU | 74.88 |
10-shot image generation > Semantic Segmentation | Fine-Grained Cloud Segmentation Dataset | KTDA | https://arxiv.org/abs/2412.06664v3 | mIoU | 51.49 |
10-shot image generation > Semantic Segmentation | Fine-Grained Cloud Segmentation Dataset | HRCloudNet | https://arxiv.org/abs/2407.07365v1 | mIoU | 43.51 |
10-shot image generation > Semantic Segmentation | ISIC 2017 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Average Dice | 98.7 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | TADP | https://arxiv.org/abs/2310.00031v3 | mIoU | 60.8 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | CoDA | https://arxiv.org/abs/2403.17369v3 | mIoU | 59.2 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | Refign (HRDA) | https://arxiv.org/abs/2207.06825v3 | mIoU | 58.0 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | Refign (DAFormer) | https://arxiv.org/abs/2207.06825v3 | mIoU | 56.8 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | MGCDA | https://arxiv.org/abs/2005.14553v2 | mIoU | 49.4 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | DANNet (PSPNet) | https://arxiv.org/abs/2104.10834v1 | mIoU | 47.70 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | GCMA | https://arxiv.org/abs/1901.05946v2 | mIoU | 45.6 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | ERF-PSPNet | https://arxiv.org/abs/1908.05868v1 | mIoU | 45.09 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | DANNet (DeepLab-v2) | https://arxiv.org/abs/2104.10834v1 | mIoU | 44.98 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | DANNet (RefineNet) | https://arxiv.org/abs/2104.10834v1 | mIoU | 42.36 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | LTSP | null | mIoU | 42.3 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | CIConv | https://arxiv.org/abs/2108.05137v2 | mIoU | 41.6 |
10-shot image generation > Semantic Segmentation | Nighttime Driving | DMAda | http://arxiv.org/abs/1810.02575v1 | mIoU | 36.1 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2011 | DLDL-8s+CRF | http://arxiv.org/abs/1611.01731v2 | Mean IoU | 67.6 |
10-shot image generation > Semantic Segmentation | Cityscapes val | ViT-P (InternImage-H) | https://arxiv.org/abs/2505.19795v1 | mIoU | 87.4 |
10-shot image generation > Semantic Segmentation | Cityscapes val | SERNet-Former | https://arxiv.org/abs/2401.15741v7 | mIoU | 87.35 |
10-shot image generation > Semantic Segmentation | Cityscapes val | SERNet-Former | https://arxiv.org/abs/2401.15741v7 | Validation mIoU | 87.35 |
10-shot image generation > Semantic Segmentation | Cityscapes val | MetaPrompt-SD | https://arxiv.org/abs/2312.14733v1 | mIoU | 87.1 |
10-shot image generation > Semantic Segmentation | Cityscapes val | InternImage-H | https://arxiv.org/abs/2211.05778v4 | mIoU | 87 |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRNetV2-OCR+PSA | https://arxiv.org/abs/2107.00782v2 | mIoU | 86.93 |
10-shot image generation > Semantic Segmentation | Cityscapes val | InternImage-XL | https://arxiv.org/abs/2211.05778v4 | mIoU | 86.4 |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRNet-OCR | https://arxiv.org/abs/2005.10821v1 | mIoU | 86.3 |
10-shot image generation > Semantic Segmentation | Cityscapes val | Depth Anything | https://arxiv.org/abs/2401.10891v2 | mIoU | 86.2 |
10-shot image generation > Semantic Segmentation | Cityscapes val | OneFormer (ConvNeXt-XL, Mapillary, multi-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 85.8 |
10-shot image generation > Semantic Segmentation | Cityscapes val | ViT-Adapter-L | https://arxiv.org/abs/2205.08534v4 | mIoU | 85.8 |
10-shot image generation > Semantic Segmentation | Cityscapes val | SeMask (SeMask Swin-L Mask2Former) | https://arxiv.org/abs/2112.12782v3 | mIoU | 84.98 |
10-shot image generation > Semantic Segmentation | Cityscapes val | Sequential Ensemble (MiT-B5 + HRNet) | https://arxiv.org/abs/2210.05387v1 | mIoU | 84.8 |
10-shot image generation > Semantic Segmentation | Cityscapes val | Soft Labells (HRnet) | https://arxiv.org/abs/2302.13961v3 | mIoU | 84.8 |
10-shot image generation > Semantic Segmentation | Cityscapes val | OneFormer (ConvNeXt-XL, multi-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 84.6 |
10-shot image generation > Semantic Segmentation | Cityscapes val | DiNAT-L (Mask2Former) | https://arxiv.org/abs/2209.15001v3 | mIoU | 84.5 |
10-shot image generation > Semantic Segmentation | Cityscapes val | OneFormer (Swin-L, multi-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 84.4 |
10-shot image generation > Semantic Segmentation | Cityscapes val | VPNeXt | https://arxiv.org/abs/2502.16654v1 | mIoU | 84.4 |
10-shot image generation > Semantic Segmentation | Cityscapes val | VOLO-D4 (MS, ImageNet1k pretrain) | https://arxiv.org/abs/2106.13112v2 | mIoU | 84.3 |
10-shot image generation > Semantic Segmentation | Cityscapes val | Mask2Former (Swin-L) | https://arxiv.org/abs/2112.01527v3 | mIoU | 84.3 |
10-shot image generation > Semantic Segmentation | Cityscapes val | EoMT (DINOv2-L, single-scale, 1024x1024) | https://arxiv.org/abs/2503.19108v1 | mIoU | 84.2 |
10-shot image generation > Semantic Segmentation | Cityscapes val | EoMT (DINOv2-L, single-scale, 1024x1024) | https://arxiv.org/abs/2503.19108v1 | FPS | 25 |
10-shot image generation > Semantic Segmentation | Cityscapes val | EoMT (DINOv2-L, single-scale, 1024x1024) | https://arxiv.org/abs/2503.19108v1 | Validation mIoU | 84.2 |
10-shot image generation > Semantic Segmentation | Cityscapes val | SegFormer (MiT-B5, Mapillary) | https://arxiv.org/abs/2105.15203v3 | mIoU | 84.0 |
10-shot image generation > Semantic Segmentation | Cityscapes val | DDP (ConvNeXt-L, step-3) | https://arxiv.org/abs/2303.17559v2 | mIoU | 83.9 |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRNetV2 + OCR + RMI (PaddleClas pretrained) | https://arxiv.org/abs/1909.11065v6 | mIoU | 83.6 |
10-shot image generation > Semantic Segmentation | Cityscapes val | PatchDiverse + Swin-L (multi-scale test, upernet, ImageNet22k pretrain) | https://arxiv.org/abs/2104.12753v3 | mIoU | 83.6% |
10-shot image generation > Semantic Segmentation | Cityscapes val | SynBoost | https://arxiv.org/abs/2103.05445v1 | mIoU | 83.5 |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRNetV2+OCR+CBL(ImageNet pretrained) | https://ieeexplore.ieee.org/document/10173725 | mIoU | 83.4 |
10-shot image generation > Semantic Segmentation | Cityscapes val | EfficientViT-B3 (r1184x2368) | https://arxiv.org/abs/2205.14756v6 | mIoU | 83.2 |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRViT-b3 (SegFormer, SS) | https://arxiv.org/abs/2111.01236v2 | mIoU | 83.16% |
10-shot image generation > Semantic Segmentation | Cityscapes val | SpineNet-S143+ (single-scale test) | https://arxiv.org/abs/2103.12270v1 | mIoU | 83.04% |
10-shot image generation > Semantic Segmentation | Cityscapes val | HRViT-b2 (SegFormer, SS) | https://arxiv.org/abs/2111.01236v2 | mIoU | 82.81% |
10-shot image generation > Semantic Segmentation | Cityscapes val | FAN-L-Hybrid+STL | https://arxiv.org/abs/2401.03844v1 | mIoU | 82.8 |
10-shot image generation > Semantic Segmentation | Cityscapes val | ResNeSt-200 | https://arxiv.org/abs/2004.08955v2 | mIoU | 82.7 |
10-shot image generation > Semantic Segmentation | Cityscapes val | WaveMix | https://arxiv.org/abs/2205.14375v5 | mIoU | 82.7 |
10-shot image generation > Semantic Segmentation | Cityscapes val | CMX (B4) | https://arxiv.org/abs/2203.04838v5 | mIoU | 82.6 |
10-shot image generation > Semantic Segmentation | Cityscapes val | WaveMix-256/16 (Level-4) | https://arxiv.org/abs/2205.14375v5 | mIoU | 82.60 |
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