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 | LaRS | BiSeNetv1 (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | F1 | 42.8 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv1 (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | μ | 73.3 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv1 (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | mIoU | 92.2 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv1 (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | Q | 39.4 |
10-shot image generation > Semantic Segmentation | LaRS | IntCatchAI | https://arxiv.org/abs/2308.09618v1 | F1 | 44.9 |
10-shot image generation > Semantic Segmentation | LaRS | IntCatchAI | https://arxiv.org/abs/2308.09618v1 | μ | 62.4 |
10-shot image generation > Semantic Segmentation | LaRS | IntCatchAI | https://arxiv.org/abs/2308.09618v1 | mIoU | 45.6 |
10-shot image generation > Semantic Segmentation | LaRS | IntCatchAI | https://arxiv.org/abs/2308.09618v1 | Q | 20.5 |
10-shot image generation > Semantic Segmentation | LaRS | UNet | https://arxiv.org/abs/2308.09618v1 | F1 | 15.4 |
10-shot image generation > Semantic Segmentation | LaRS | UNet | https://arxiv.org/abs/2308.09618v1 | μ | 75.7 |
10-shot image generation > Semantic Segmentation | LaRS | UNet | https://arxiv.org/abs/2308.09618v1 | mIoU | 90.1 |
10-shot image generation > Semantic Segmentation | LaRS | UNet | https://arxiv.org/abs/2308.09618v1 | Q | 13.9 |
10-shot image generation > Semantic Segmentation | Freiburg Forest | SSMA | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 84.18 |
10-shot image generation > Semantic Segmentation | Freiburg Forest | AdapNet++ | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 83.09 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | UANet(PVT-V2-B2) | https://arxiv.org/abs/2307.12309v1 | IoU | 83.34 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | UANet(Re2sNet50) | https://arxiv.org/abs/2307.12309v1 | IoU | 83.17 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | UANet(VGG-16) | https://arxiv.org/abs/2307.12309v1 | IoU | 83.08 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | SDSC-UNet | https://ieeexplore.ieee.org/document/10108049 | IoU | 83.01 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | DSAT-Net | https://ieeexplore.ieee.org/document/10221771 | IoU | 82.68 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | UANet(ResNet50) | https://arxiv.org/abs/2307.12309v1 | IoU | 82.17 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | ICT-Net | https://arxiv.org/abs/1912.09216v1 | IoU | 80.32 |
10-shot image generation > Semantic Segmentation | INRIA Aerial Image Labeling | WSDNet | https://arxiv.org/abs/2305.10899v1 | mIOU | 0.752 |
10-shot image generation > Semantic Segmentation | FMB Dataset | RoadFormer+ (RGB-Infrared) | https://arxiv.org/abs/2407.21631v2 | mIoU | 73.1 |
10-shot image generation > Semantic Segmentation | FMB Dataset | SHIFNet (RGB-Infrared) | https://arxiv.org/abs/2503.02581v1 | mIoU | 67.8 |
10-shot image generation > Semantic Segmentation | FMB Dataset | StitchFusion+FFMs (RGB-Infrared) | https://arxiv.org/abs/2408.01343v1 | mIoU | 64.32 |
10-shot image generation > Semantic Segmentation | FMB Dataset | StitchFusion (RGB-Infrared) | https://arxiv.org/abs/2408.01343v1 | mIoU | 63.30 |
10-shot image generation > Semantic Segmentation | FMB Dataset | MMSFormer (RGB-Infrared) | https://arxiv.org/abs/2309.04001v4 | mIoU | 61.70 |
10-shot image generation > Semantic Segmentation | FMB Dataset | MMSFormer (RGB) | https://arxiv.org/abs/2309.04001v4 | mIoU | 57.20 |
10-shot image generation > Semantic Segmentation | FMB Dataset | SegMiF (RGB-Infrared) | https://arxiv.org/abs/2308.02097v1 | mIoU | 54.80 |
10-shot image generation > Semantic Segmentation | FMB Dataset | ReCoNet (RGB-Infrared) | http://arxiv.org/abs/1807.01197v2 | mIoU | 50.90 |
10-shot image generation > Semantic Segmentation | FMB Dataset | DIDFuse (RGB-Infrared) | https://arxiv.org/abs/2003.09210v3 | mIoU | 50.60 |
10-shot image generation > Semantic Segmentation | FMB Dataset | SegMiF (RGB) | https://arxiv.org/abs/2308.02097v1 | mIoU | 50.50 |
10-shot image generation > Semantic Segmentation | FMB Dataset | GMNet (RGB-Infrared) | https://arxiv.org/abs/2007.09073v1 | mIoU | 49.20 |
10-shot image generation > Semantic Segmentation | FMB Dataset | TarDAL (RGB-Infrared) | https://arxiv.org/abs/2203.16220v1 | mIoU | 48.10 |
10-shot image generation > Semantic Segmentation | FMB Dataset | EGFNet (RGB-Infrared) | https://ieeexplore.ieee.org/abstract/document/10234530 | mIoU | 47.30 |
10-shot image generation > Semantic Segmentation | FMB Dataset | FEANet (RGB-Infrared) | https://arxiv.org/abs/2110.08988v1 | mIoU | 46.80 |
10-shot image generation > Semantic Segmentation | Semantic3D | Feature Geometric Net | https://arxiv.org/abs/2012.09439v2 | mIoU | 78.2% |
10-shot image generation > Semantic Segmentation | Semantic3D | Feature Geometric Net | https://arxiv.org/abs/2012.09439v2 | oAcc | 93.6 |
10-shot image generation > Semantic Segmentation | Semantic3D | RFCR | https://arxiv.org/abs/2105.10203v1 | mIoU | 77.8% |
10-shot image generation > Semantic Segmentation | Semantic3D | SCF-Net | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_SCF-Net_Learning_Spatial_Contextual_Features_for_Large-Scale_Point_Cloud_Segmentation_CVPR_2021_paper.html | mIoU | 77.6% |
10-shot image generation > Semantic Segmentation | Semantic3D | SCF-Net | http://openaccess.thecvf.com//content/CVPR2021/html/Fan_SCF-Net_Learning_Spatial_Contextual_Features_for_Large-Scale_Point_Cloud_Segmentation_CVPR_2021_paper.html | oAcc | 94.7% |
10-shot image generation > Semantic Segmentation | Semantic3D | RandLA-Net | https://arxiv.org/abs/1911.11236v3 | mIoU | 77.4% |
10-shot image generation > Semantic Segmentation | Semantic3D | RandLA-Net | https://arxiv.org/abs/1911.11236v3 | oAcc | 94.8 |
10-shot image generation > Semantic Segmentation | Semantic3D | SPG | http://arxiv.org/abs/1711.09869v2 | mIoU | 76.2% |
10-shot image generation > Semantic Segmentation | Semantic3D | SPG | http://arxiv.org/abs/1711.09869v2 | oAcc | 92.9% |
10-shot image generation > Semantic Segmentation | Semantic3D | BAAF-Net | https://arxiv.org/abs/2103.07074v2 | mIoU | 75.4% |
10-shot image generation > Semantic Segmentation | Semantic3D | BAAF-Net | https://arxiv.org/abs/2103.07074v2 | oAcc | 94.9% |
10-shot image generation > Semantic Segmentation | Semantic3D | KPConv | https://arxiv.org/abs/1904.08889v2 | mIoU | 74.6% |
10-shot image generation > Semantic Segmentation | Semantic3D | SPG | http://arxiv.org/abs/1711.09869v2 | mIoU | 73.2% |
10-shot image generation > Semantic Segmentation | Semantic3D | GACNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Graph_Attention_Convolution_for_Point_Cloud_Semantic_Segmentation_CVPR_2019_paper.html | mIoU | 70.8% |
10-shot image generation > Semantic Segmentation | Semantic3D | shellnet_v2 | https://arxiv.org/abs/1908.06295v1 | mIoU | 69.3% |
10-shot image generation > Semantic Segmentation | Semantic3D | MSDeepVoxNet | http://arxiv.org/abs/1804.03583v1 | mIoU | 65.3% |
10-shot image generation > Semantic Segmentation | Semantic3D | RF_MSSF | http://arxiv.org/abs/1808.00495v1 | mIoU | 62.7% |
10-shot image generation > Semantic Segmentation | Semantic3D | SegCloud | http://arxiv.org/abs/1710.07563v1 | mIoU | 61.3% |
10-shot image generation > Semantic Segmentation | Semantic3D | SnapNet_ | http://blesaux.free.fr/papers/17-EG3DOR-SnapNet-BoulchLeSauxAudebert-compressed.pdf | mIoU | 59.1% |
10-shot image generation > Semantic Segmentation | Semantic3D | DeePr3SS | http://arxiv.org/abs/1705.03428v1 | mIoU | 58.5% |
10-shot image generation > Semantic Segmentation | Semantic3D | 3D-FCNN-TI | http://arxiv.org/abs/1710.07563v1 | mIoU | 58.2% |
10-shot image generation > Semantic Segmentation | Semantic3D | TMLC-MSR | https://www.ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/timo-jan-isprs2016.pdf | mIoU | 54.2% |
10-shot image generation > Semantic Segmentation | SpectralWaste | CMX (RGB-HYPER) | https://arxiv.org/abs/2203.04838v5 | mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | SpectralWaste | CMX ( RGB-HYPER3 ) | https://arxiv.org/abs/2203.04838v5 | mIoU | 56.6 |
10-shot image generation > Semantic Segmentation | SpectralWaste | SegFormer (HYPER) | https://arxiv.org/abs/2105.15203v3 | mIoU | 54.3 |
10-shot image generation > Semantic Segmentation | SpectralWaste | SegFormer (HYPER3) | https://arxiv.org/abs/2105.15203v3 | mIoU | 53.5 |
10-shot image generation > Semantic Segmentation | SpectralWaste | MiniNet (HYPER) | https://arxiv.org/abs/2006.15350v1 | mIoU | 52.8 |
10-shot image generation > Semantic Segmentation | SpectralWaste | MiniNet (HYPER3) | https://arxiv.org/abs/2006.15350v1 | mIoU | 49.0 |
10-shot image generation > Semantic Segmentation | SpectralWaste | SegFormer (RGB) | https://arxiv.org/abs/2105.15203v3 | mIoU | 48.4 |
10-shot image generation > Semantic Segmentation | SpectralWaste | MiniNet (RGB) | https://arxiv.org/abs/2006.15350v1 | mIoU | 44.5 |
10-shot image generation > Semantic Segmentation | Cityscapes VIPriors subset | EfficientSeg | https://arxiv.org/abs/2009.06469v2 | mIoU | 58.03 |
10-shot image generation > Semantic Segmentation | Cityscapes VIPriors subset | EfficientSeg | https://arxiv.org/abs/2009.06469v2 | Accuracy | 81.68 |
10-shot image generation > Semantic Segmentation | PETRAW | NCC Next | https://arxiv.org/abs/2202.05821v3 | Mean IoU (class) | 96.9 |
10-shot image generation > Semantic Segmentation | PETRAW | SK | https://arxiv.org/abs/2202.05821v3 | Mean IoU (class) | 96.4 |
10-shot image generation > Semantic Segmentation | PETRAW | MediCIS | https://arxiv.org/abs/2202.05821v3 | Mean IoU (class) | 94 |
10-shot image generation > Semantic Segmentation | PETRAW | Hutom | https://arxiv.org/abs/2202.05821v3 | Mean IoU (class) | 85 |
10-shot image generation > Semantic Segmentation | iSAID | SegNeXt-L | https://arxiv.org/abs/2209.08575v1 | mIoU | 70.3 |
10-shot image generation > Semantic Segmentation | iSAID | SegNeXt-B | https://arxiv.org/abs/2209.08575v1 | mIoU | 69.9 |
10-shot image generation > Semantic Segmentation | iSAID | AerialFormer-B | https://arxiv.org/abs/2306.06842v2 | mIoU | 69.3 |
10-shot image generation > Semantic Segmentation | iSAID | SegNeXt-S | https://arxiv.org/abs/2209.08575v1 | mIoU | 68.8 |
10-shot image generation > Semantic Segmentation | iSAID | AerialFormer-S | https://arxiv.org/abs/2306.06842v2 | mIoU | 68.4 |
10-shot image generation > Semantic Segmentation | iSAID | SegNeXt-T | https://arxiv.org/abs/2209.08575v1 | mIoU | 68.3 |
10-shot image generation > Semantic Segmentation | iSAID | FarSeg++@MiT-B2 | https://ieeexplore.ieee.org/document/10188509 | mIoU | 67.9 |
10-shot image generation > Semantic Segmentation | iSAID | FarSeg++@ResNet-50 | https://ieeexplore.ieee.org/document/10188509 | mIoU | 67.6 |
10-shot image generation > Semantic Segmentation | iSAID | AerialFormer-T | https://arxiv.org/abs/2306.06842v2 | mIoU | 67.5 |
10-shot image generation > Semantic Segmentation | iSAID | DeepLabV3 with R-50 | https://arxiv.org/abs/2307.14179v1 | mIoU | 67.03 |
10-shot image generation > Semantic Segmentation | iSAID | FarSeg++@Swin-T | https://ieeexplore.ieee.org/document/10188509 | mIoU | 66.3 |
10-shot image generation > Semantic Segmentation | iSAID | IMP-ViTAEv2-S-UperNet | https://arxiv.org/abs/2204.02825v4 | mIoU | 65.3 |
10-shot image generation > Semantic Segmentation | iSAID | FactSeg@ResNet-50 | https://ieeexplore.ieee.org/document/9497514 | mIoU | 64.79 |
10-shot image generation > Semantic Segmentation | iSAID | ViTAE-B + RVSA-UperNet | https://arxiv.org/abs/2208.03987v4 | mIoU | 64.49 |
10-shot image generation > Semantic Segmentation | iSAID | RSP-ViTAEv2-S-UperNet | https://arxiv.org/abs/2204.02825v4 | mIoU | 64.3 |
10-shot image generation > Semantic Segmentation | iSAID | RSP-Swin-T-UperNet | https://arxiv.org/abs/2204.02825v4 | mIoU | 64.1 |
10-shot image generation > Semantic Segmentation | iSAID | ViT-B + RVSA-UperNet | https://arxiv.org/abs/2208.03987v4 | mIoU | 63.85 |
10-shot image generation > Semantic Segmentation | iSAID | FarSeg@ResNet-50 | https://arxiv.org/abs/2011.09766v1 | mIoU | 63.71 |
10-shot image generation > Semantic Segmentation | iSAID | RSP-ResNet-50-UperNet | https://arxiv.org/abs/2204.02825v4 | mIoU | 61.6 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 39.19 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 35.92 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 38.91 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 35.77 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 39.26 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 35.52 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 35.15 |
10-shot image generation > Semantic Segmentation | Matterport3D | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 31.3 |
10-shot image generation > Semantic Segmentation | PASTIS-R | Late Fusion | https://arxiv.org/abs/2112.07558v1 | IoU | 66.3 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.