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 | Kvasir-Instrument | UNet | http://arxiv.org/abs/1505.04597v1 | mIoU | 0.8578 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2011 test | Plugin network | https://arxiv.org/abs/1901.00326v3 | Mean IoU | 72.2 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2011 test | FCN-VGG16 | http://arxiv.org/abs/1605.06211v1 | Mean IoU | 32 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2011 test | FCN-pool4 | http://arxiv.org/abs/1605.06211v1 | Mean IoU | 22.4 |
10-shot image generation > Semantic Segmentation | Porto | CMNeXt | https://arxiv.org/abs/2303.01480v1 | IoU | 73.12 |
10-shot image generation > Semantic Segmentation | Porto | CMX | https://arxiv.org/abs/2203.04838v5 | IoU | 72.85 |
10-shot image generation > Semantic Segmentation | Porto | CMMPNet | https://arxiv.org/abs/2111.15119v3 | IoU | 72.29 |
10-shot image generation > Semantic Segmentation | Porto | SA-Gate | https://arxiv.org/abs/2007.09183v1 | IoU | 72.21 |
10-shot image generation > Semantic Segmentation | Porto | Sun et al. | https://arxiv.org/abs/1905.01447v1 | IoU | 71.79 |
10-shot image generation > Semantic Segmentation | Porto | D-LinkNet | https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf | IoU | 70.20 |
10-shot image generation > Semantic Segmentation | DeLiVER test | CAFuser | https://arxiv.org/abs/2410.10791v2 | mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | Cityscapes 3D | TaskPrompter | https://arxiv.org/abs/2304.00971v3 | mIoU | 77.72 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | D2LS | https://arxiv.org/abs/2503.06683v1 | mIoU | 51.96 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | SFA-Net | https://www.mdpi.com/2072-4292/16/17/3278 | mIoU | 51.21 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | KTDA | https://arxiv.org/abs/2412.06664v3 | mIoU | 50.86 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU | 48.29 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | UNet | http://arxiv.org/abs/1505.04597v1 | mIoU | 48.17 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 47.95 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | DeepLabv3+ | http://arxiv.org/abs/1802.02611v3 | mIoU | 47.95 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | DINOv2 | https://arxiv.org/abs/2304.07193v2 | mIoU | 47.57 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | FCN | http://arxiv.org/abs/1411.4038v2 | mIoU | 47.47 |
10-shot image generation > Semantic Segmentation | Fine-Grained Grass Segmentation Dataset | Mask2Former | https://arxiv.org/abs/2112.01527v3 | mIoU | 44.93 |
10-shot image generation > Semantic Segmentation | Lombardia Sentinel-2 Image Time Series for Crop Mapping | UNet3D | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 80.77 |
10-shot image generation > Semantic Segmentation | Lombardia Sentinel-2 Image Time Series for Crop Mapping | Swin UNETR | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 79.64 |
10-shot image generation > Semantic Segmentation | Lombardia Sentinel-2 Image Time Series for Crop Mapping | 3D FPN with NDVI Loss | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 77.23 |
10-shot image generation > Semantic Segmentation | Lombardia Sentinel-2 Image Time Series for Crop Mapping | DeepLabv3 3D | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13072/1307208/Enhancing-crop-segmentation-in-satellite-image-time-series-with-transformer/10.1117/12.3023389.short#_=_ | Overall Accuracy | 74.51 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | RPVNet [xu2021rpvnet] | https://arxiv.org/abs/2303.12766v1 | Mean IoU (class) | 80.7 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | DeepLabV3Plus + SDCNetAug | https://arxiv.org/abs/1812.01593v3 | Mean IoU (class) | 72.83 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | DeepLabV3Plus + SDCNetAug | https://arxiv.org/abs/1812.01593v3 | class iIoU | 48.68 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | DeepLabV3Plus + SDCNetAug | https://arxiv.org/abs/1812.01593v3 | Category IoU | 88.99 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | DeepLabV3Plus + SDCNetAug | https://arxiv.org/abs/1812.01593v3 | Category iIoU | 75.26 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | MapillaryAI | http://arxiv.org/abs/1712.02616v3 | Mean IoU (class) | 69.56 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | SIW | https://arxiv.org/abs/2202.02002v2 | Mean IoU (class) | 68.9 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | AHiSS | http://arxiv.org/abs/1803.05675v2 | Mean IoU (class) | 61.24 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | SegStereo | http://arxiv.org/abs/1807.11699v1 | Mean IoU (class) | 59.10 |
10-shot image generation > Semantic Segmentation | KITTI Semantic Segmentation | APMoE_seg | http://arxiv.org/abs/1805.01556v2 | Mean IoU (class) | 47.96 |
10-shot image generation > Semantic Segmentation | Replica | LabelMaker | https://arxiv.org/abs/2311.12174v1 | mIoU | 42.1 |
10-shot image generation > Semantic Segmentation | Replica | InternImage | https://arxiv.org/abs/2211.05778v4 | mIoU | 38.4 |
10-shot image generation > Semantic Segmentation | Replica | Mask3D | https://arxiv.org/abs/2210.03105v2 | mIoU | 22.6 |
10-shot image generation > Semantic Segmentation | Replica | OVSeg | https://arxiv.org/abs/2210.04150v3 | mIoU | 20.7 |
10-shot image generation > Semantic Segmentation | Replica | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 17.0 |
10-shot image generation > Semantic Segmentation | ARCH2S | BIM-Net | https://ieeexplore.ieee.org/document/10222064 | mIoU | 18.4 |
10-shot image generation > Semantic Segmentation | LoveDA | U-Net (MaxViT-S) | https://www.mdpi.com/2072-4292/16/12/2077 | Category mIoU | 56.16 |
10-shot image generation > Semantic Segmentation | LoveDA | D2LS | https://arxiv.org/abs/2503.06683v1 | Category mIoU | 55.3 |
10-shot image generation > Semantic Segmentation | LoveDA | SFA-Net | https://www.mdpi.com/2072-4292/16/17/3278 | Category mIoU | 54.9 |
10-shot image generation > Semantic Segmentation | LoveDA | ViT-G12X4 | https://arxiv.org/abs/2304.05215v4 | Category mIoU | 54.4 |
10-shot image generation > Semantic Segmentation | LoveDA | SelectiveMAE+ViT-L | https://arxiv.org/abs/2406.11933v4 | Category mIoU | 54.31 |
10-shot image generation > Semantic Segmentation | LoveDA | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | Category mIoU | 54.17 |
10-shot image generation > Semantic Segmentation | LoveDA | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | Category mIoU | 54.17 |
10-shot image generation > Semantic Segmentation | LoveDA | AerialFormer-B | https://arxiv.org/abs/2306.06842v2 | Category mIoU | 54.1 |
10-shot image generation > Semantic Segmentation | LoveDA | LSKNet-S | https://arxiv.org/abs/2303.09030v2 | Category mIoU | 54.0 |
10-shot image generation > Semantic Segmentation | LoveDA | LWGANet L2 | https://arxiv.org/abs/2501.10040v1 | Category mIoU | 53.6 |
10-shot image generation > Semantic Segmentation | LoveDA | LOGCAN++ | https://arxiv.org/abs/2406.16502v2 | Category mIoU | 53.35 |
10-shot image generation > Semantic Segmentation | LoveDA | LSKNet-T | https://arxiv.org/abs/2303.09030v2 | Category mIoU | 53.2 |
10-shot image generation > Semantic Segmentation | LoveDA | DecoupleNet D2 | https://ieeexplore.ieee.org/document/10685518 | Category mIoU | 53.1 |
10-shot image generation > Semantic Segmentation | LoveDA | Hi-ResNet | https://arxiv.org/abs/2305.12691v3 | Category mIoU | 52.6 |
10-shot image generation > Semantic Segmentation | LoveDA | ViTAE-B + RVSA-UperNet | https://arxiv.org/abs/2208.03987v4 | Category mIoU | 52.44 |
10-shot image generation > Semantic Segmentation | LoveDA | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Category mIoU | 52.40 |
10-shot image generation > Semantic Segmentation | LoveDA | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | Category mIoU | 52.39 |
10-shot image generation > Semantic Segmentation | LoveDA | ViT-B + RVSA-UperNet | https://arxiv.org/abs/2208.03987v4 | Category mIoU | 51.95 |
10-shot image generation > Semantic Segmentation | LoveDA | HRNetw32 | https://arxiv.org/abs/2110.08733v6 | Category mIoU | 49.79 |
10-shot image generation > Semantic Segmentation | Montgomery County X-ray Set | UNETR + SS-CXR | https://arxiv.org/abs/2211.12944v2 | F1-score | 0.9561 |
10-shot image generation > Semantic Segmentation | Montgomery County X-ray Set | UNETR+ SS-IN | https://arxiv.org/abs/2211.12944v2 | F1-score | 0.9453 |
10-shot image generation > Semantic Segmentation | Montgomery County X-ray Set | UNETR | https://arxiv.org/abs/2211.12944v2 | F1-score | 0.9227 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | GeminiFusion (Swin-Large) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 54.6 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DiffusionMMS | https://arxiv.org/abs/2409.15117v2 | Mean IoU | 54.0 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | HDBFormer | https://arxiv.org/abs/2504.13579v1 | Mean IoU | 53.9% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | GeminiFusion (MiT-B5) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 53.3 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormerv2-L | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 53.3 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (S) | https://arxiv.org/abs/2204.08721v2 | Mean IoU | 53.0% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DPLNet | https://arxiv.org/abs/2312.00360v2 | Mean IoU | 52.8% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormerv2-B | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 52.8% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | GeminiFusion (MiT-B3) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 52.7 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-L | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 52.5% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B5) | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 52.4% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B4) | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 52.1% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormerv2-S | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 51.5% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (Ti) | https://arxiv.org/abs/2204.08721v2 | Mean IoU | 51.4% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-B | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 51.2% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned) | https://arxiv.org/abs/2309.13635v2 | Mean IoU | 50.86% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | FSFNet | https://arxiv.org/abs/2105.04102v1 | Mean IoU | 50.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | PSD-ResNet50 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Pattern-Structure_Diffusion_for_Multi-Task_Learning_CVPR_2020_paper.html | Mean IoU | 50.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (S) | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 50.0% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DPLNet | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 49.7% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-L | https://arxiv.org/abs/2201.01427v2 | Mean IoU | 49.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B5) | https://arxiv.org/abs/2210.06747v1 | Mean IoU | 49.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B4) | https://arxiv.org/abs/2302.11951v1 | Mean IoU | 49.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (Ti) | https://arxiv.org/abs/2007.09183v1 | Mean IoU | 49.4% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-B | https://arxiv.org/abs/2309.14065v7 | Mean IoU | 49.1% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned) | https://arxiv.org/abs/2306.05242v1 | Mean IoU | 48.82% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | FSFNet | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 48.8% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | PSD-ResNet50 | https://arxiv.org/abs/2108.10528v1 | Mean IoU | 48.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (S) | https://arxiv.org/abs/2004.04534v2 | Mean IoU | 48.6% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DPLNet | https://arxiv.org/abs/2207.04526v1 | Mean IoU | 48.47% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-L | https://arxiv.org/abs/2002.12041v4 | Mean IoU | 48.3% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B5) | https://arxiv.org/abs/2011.06961v3 | Mean IoU | 48.17 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | CMX (B4) | https://arxiv.org/abs/1905.10089v1 | Mean IoU | 48.1% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (Ti) | http://arxiv.org/abs/1806.01054v2 | Mean IoU | 47.8% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-B | http://openaccess.thecvf.com/content_iccv_2017/html/Park_RDFNet_RGB-D_Multi-Level_ICCV_2017_paper.html | Mean IoU | 47.7% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned) | http://openaccess.thecvf.com/content_cvpr_2018/html/Ding_Context_Contrasted_Feature_CVPR_2018_paper.html | Mean IoU | 47.1% |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.