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 | DFormerv2-L | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 58.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SwinMTL | https://arxiv.org/abs/2403.10662v1 | Mean IoU | 58.14% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PolyMaX(ConvNeXt-L) | https://arxiv.org/abs/2311.05770v1 | Mean IoU | 58.08% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HSPFormer(PVT v2-B4) | https://doi.org/10.1109/TITS.2025.3525542 | Mean IoU | 57.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | GeminiFusion (MiT-B5) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 57.7 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormerv2-B | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 57.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormer-L | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 57.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CMX (B5) | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 56.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CMNeXt (B4) | https://arxiv.org/abs/2303.01480v1 | Mean IoU | 56.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | OMNIVORE (Swin-L, finetuned) | https://arxiv.org/abs/2201.08377v2 | Mean IoU | 56.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | GeminiFusion (MiT-B3) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 56.8 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CMX (B4) | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 56.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MultiMAE (ViT-B) | https://arxiv.org/abs/2204.01678v1 | Mean IoU | 56.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormerv2-S | https://arxiv.org/abs/2504.04701v1 | Mean IoU | 56.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SMMCL (SegNeXt-B) | https://arxiv.org/abs/2308.12320v2 | Mean IoU | 55.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormer-B | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 55.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ComPtr (Swin-B) | https://arxiv.org/abs/2307.12349v1 | Mean IoU | 55.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | AsymFormer | https://arxiv.org/abs/2309.14065v7 | Mean IoU | 55.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | OMNIVORE (Swin-B, finetuned) | https://arxiv.org/abs/2201.08377v2 | Mean IoU | 55.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HAPNet | https://arxiv.org/abs/2404.03527v2 | Mean IoU | 55.0 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HAPNet | https://arxiv.org/abs/2404.03527v2 | Mean Accuracy | 68.8 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CMX (B2) | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 54.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TokenFusion (S) | https://arxiv.org/abs/2204.08721v2 | Mean IoU | 54.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SMMCL (SegFormer-B2) | https://arxiv.org/abs/2308.12320v2 | Mean IoU | 53.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormer-S | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 53.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | InvPT | https://arxiv.org/abs/2203.07997v3 | Mean IoU | 53.56% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HS3-Fuse (ResNet-101) | https://arxiv.org/abs/2111.02333v1 | Mean IoU | 53.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PDCNet (ResNet-101) | https://arxiv.org/abs/2302.11951v1 | Mean IoU | 53.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | EMSANet (2x ResNet-34 NBt1D, finetuned) | https://arxiv.org/abs/2207.04526v1 | Mean IoU | 53.34% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DCANet (ResNet-101) | https://arxiv.org/abs/2210.06747v1 | Mean IoU | 53.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TokenFusion (Ti) | https://arxiv.org/abs/2204.08721v2 | Mean IoU | 53.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | InverseForm (ResNet-101) | https://arxiv.org/abs/2104.02745v2 | Mean IoU | 53.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CAINet (MobileNet-V2) | https://arxiv.org/abs/2401.01624v1 | Mean IoU | 52.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CEN-PSPNet (ResNet-152) | https://arxiv.org/abs/2112.02252v2 | Mean IoU | 52.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | AMF (ResNet-50) | https://arxiv.org/abs/2201.01427v2 | Mean IoU | 52.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SMMCL (ResNet-101) | https://arxiv.org/abs/2308.12320v2 | Mean IoU | 52.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SA-Gate | https://arxiv.org/abs/2007.09183v1 | Mean IoU | 52.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Warp-Refine | https://arxiv.org/abs/2109.13432v1 | Mean IoU | 52.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | FSFNet | https://arxiv.org/abs/2105.04102v1 | Mean IoU | 52.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | VCD+ACNet (ResNet-50) | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | Mean IoU | 51.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MIPANet (ResNet50) | https://arxiv.org/abs/2311.11312v2 | Mean IoU | 51.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DFormer-T | https://arxiv.org/abs/2309.09668v2 | Mean IoU | 51.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ShapeConv (ResNext-101) | https://arxiv.org/abs/2108.10528v1 | Mean IoU | 51.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | EMSAFormer (SwinV2-T-128-Multi-Aug) | https://arxiv.org/abs/2306.05242v1 | Mean IoU | 51.26% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Z-ACN (ResNet-101) | https://arxiv.org/abs/2206.03939v1 | Mean IoU | 51.24% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | AsymFusion (ResNet-152) | https://arxiv.org/abs/2108.05009v1 | Mean IoU | 51.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DynMM (ResNet-50) | https://arxiv.org/abs/2204.00102v2 | Mean IoU | 51.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SGNet (ResNet-101) | https://arxiv.org/abs/2004.04534v2 | Mean IoU | 51.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PSD-ResNet50 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Pattern-Structure_Diffusion_for_Multi-Task_Learning_CVPR_2020_paper.html | Mean IoU | 51.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Malleable 2.5D (ResNet-101) | https://arxiv.org/abs/2007.09365v1 | Mean IoU | 50.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ICM | http://openaccess.thecvf.com/content_CVPR_2019/html/Shi_Scene_Parsing_via_Integrated_Classification_Model_and_Variance-Based_Regularization_CVPR_2019_paper.html | Mean IoU | 50.70 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SANet | https://arxiv.org/abs/2011.02572v1 | Mean IoU | 50.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | VCD+RedNet (ResNet-50) | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | Mean IoU | 50.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HaarNet | https://arxiv.org/abs/2310.07669v1 | Mean IoU | 50.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PAP (ResNet-50) | https://arxiv.org/abs/1906.03525v1 | Mean IoU | 50.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Cerberus | https://arxiv.org/abs/2111.12608v2 | Mean IoU | 50.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ESANet (R34-NBt1D) | https://arxiv.org/abs/2011.06961v3 | Mean IoU | 50.30 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Z-ACN (ResNet-50) | https://arxiv.org/abs/2206.03939v1 | Mean IoU | 50.05% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Malleable 2.5D (ResNet-50) | https://arxiv.org/abs/2007.09365v1 | Mean IoU | 49.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MMANet | https://arxiv.org/abs/2304.08028v1 | Mean IoU | 49.62% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SGACNet (R34-NBt1D) | https://arxiv.org/abs/2308.06024v1 | Mean IoU | 49.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ComPtr (Swin-T) | https://arxiv.org/abs/2307.12349v1 | Mean IoU | 49.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Z-ACN (ResNet-34) | https://arxiv.org/abs/2206.03939v1 | Mean IoU | 49.15% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | UMT | https://arxiv.org/abs/2106.11059v1 | Mean IoU | 49.14% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MTI-Net (HRNet-48) | https://arxiv.org/abs/2001.06902v5 | Mean IoU | 49.0 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ShapeConv (ResNet-101) | https://arxiv.org/abs/2108.10528v1 | Mean IoU | 49.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MKE | https://arxiv.org/abs/2103.14431v3 | Mean IoU | 48.88% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ShapeConv (ResNet-50) | https://arxiv.org/abs/2108.10528v1 | Mean IoU | 48.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | mmFormer | https://arxiv.org/abs/2206.02425v2 | Mean IoU | 48.45% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ACNet | https://arxiv.org/abs/1905.10089v1 | Mean IoU | 48.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SGACNet (R18-NBt1D) | https://arxiv.org/abs/2308.06024v1 | Mean IoU | 48.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ESANet (R18-NBt1D ) | https://arxiv.org/abs/2011.06961v3 | Mean IoU | 48.17 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | RFNet | http://openaccess.thecvf.com//content/ICCV2021/html/Ding_RFNet_Region-Aware_Fusion_Network_for_Incomplete_Multi-Modal_Brain_Tumor_Segmentation_ICCV_2021_paper.html | Mean IoU | 48.13% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TupleInfoNCE | https://arxiv.org/abs/2107.02575v1 | Mean IoU | 48.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DDSC (ResNet-101) | http://openaccess.thecvf.com/content_cvpr_2018/html/Bilinski_Dense_Decoder_Shortcut_CVPR_2018_paper.html | Mean IoU | 48.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CFN | http://openaccess.thecvf.com/content_iccv_2017/html/Lin_Cascaded_Feature_Network_ICCV_2017_paper.html | Mean IoU | 47.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | FDNet (DenseNet264) | https://arxiv.org/abs/1905.08929v1 | Mean IoU | 47.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | RedNet | http://arxiv.org/abs/1806.01054v2 | Mean IoU | 47.2% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Z-ACN (ResNet-18) | https://arxiv.org/abs/2206.03939v1 | Mean IoU | 47.02% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TRL (ResNet-101) | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhenyu_Zhang_Joint_Task-Recursive_Learning_ECCV_2018_paper.html | Mean IoU | 46.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | RefineNet (ResNet-101) | http://arxiv.org/abs/1611.06612v3 | Mean IoU | 46.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PGT (Swin-S) | https://arxiv.org/abs/2307.15362v1 | Mean IoU | 46.43 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | ATRC | https://arxiv.org/abs/2104.13874v2 | Mean IoU | 46.33% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | LS-DeconvNet | http://openaccess.thecvf.com/content_cvpr_2017/html/Cheng_Locality-Sensitive_Deconvolution_Networks_CVPR_2017_paper.html | Mean IoU | 45.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | VCD+DeepLab (VGG16) | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | Mean IoU | 45.3 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | SOSD-Net | https://arxiv.org/abs/2101.07422v1 | Mean IoU | 45.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MMAF-Net-152 | https://arxiv.org/abs/1912.11691v1 | Mean IoU | 44.8% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | RecurrentSceneParsing | http://arxiv.org/abs/1705.07238v2 | Mean IoU | 44.5% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Light-Weight-RefineNet-152 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 44.4% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Depth-aware CNN | http://arxiv.org/abs/1803.06791v1 | Mean IoU | 43.9% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Light-Weight-RefineNet-101 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 43.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | TD2-PSP50 | https://arxiv.org/abs/2004.01800v2 | Mean IoU | 43.5 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | NDDR-CNN | http://arxiv.org/abs/1801.08297v4 | Mean IoU | 43.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | 3DGNN | http://openaccess.thecvf.com/content_iccv_2017/html/Qi_3D_Graph_Neural_ICCV_2017_paper.html | Mean IoU | 43.1% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | CI-Net | https://arxiv.org/abs/2107.13800v2 | Mean IoU | 42.6% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Multi-Task Light-Weight-RefineNet | http://arxiv.org/abs/1809.04766v2 | Mean IoU | 42.0% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | Light-Weight-RefineNet-50 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 41.7% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | PGT (Swin-T) | https://arxiv.org/abs/2307.15362v1 | Mean IoU | 41.61 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | MTML | https://arxiv.org/abs/2210.06989v4 | Mean IoU | 41.51% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | RAN | http://arxiv.org/abs/1707.06426v1 | Mean IoU | 41.2% |
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