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 | Babies | TGAN + ADA | https://arxiv.org/abs/2006.06676v2 | FID | 97.91 |
10-shot image generation | Babies | TGAN | http://arxiv.org/abs/1805.01677v2 | FID | 101.58 |
10-shot image generation | FlyingThings3D | 1 | https://arxiv.org/abs/2206.09379v2 | 0..5sec | 1 |
10-shot image generation | MEAD | 12 | https://arxiv.org/abs/1912.02315v2 | 12k | 12 |
10-shot image generation | . | 10 Ways to Contact: How Can I Talk to Someone at Allegiant Airlines® – A Step-by-Step Guide | http://arxiv.org/abs/1409.3660v4 | 10-20% Mask PSNR | 10 Ways to Contact: How Can I Talk to Someone at Allegiant Airlines® – A Step-by-Step Guide |
10-shot image generation | FQL-Driving | FQL | https://arxiv.org/abs/2309.16292v3 | 0-shot MRR | 1 |
10-shot image generation | Music21 | song | https://arxiv.org/abs/2402.04499v2 | 0..5sec | 5 |
10-shot image generation > Semantic Segmentation | US3D | LMFNet-3 | https://arxiv.org/abs/2404.13659v1 | mIoU | 85.09 |
10-shot image generation > Semantic Segmentation | US3D | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 84.63 |
10-shot image generation > Semantic Segmentation | US3D | LMFNet-2 | https://arxiv.org/abs/2404.13659v1 | mIoU | 84.50 |
10-shot image generation > Semantic Segmentation | US3D | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU | 83.62 |
10-shot image generation > Semantic Segmentation | US3D | vFuseNet | http://arxiv.org/abs/1711.08681v1 | mIoU | 83.53 |
10-shot image generation > Semantic Segmentation | US3D | SegFormer-B2 | https://arxiv.org/abs/2105.15203v3 | mIoU | 75.14 |
10-shot image generation > Semantic Segmentation | US3D | UNetFormer | https://arxiv.org/abs/2109.08937v4 | mIoU | 74.77 |
10-shot image generation > Semantic Segmentation | US3D | SegFormer-B1 | https://arxiv.org/abs/2105.15203v3 | mIoU | 74.19 |
10-shot image generation > Semantic Segmentation | US3D | PSNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 73.12 |
10-shot image generation > Semantic Segmentation | US3D | FPN | http://arxiv.org/abs/1612.03144v2 | mIoU | 72.51 |
10-shot image generation > Semantic Segmentation | US3D | SegFormer-B0 | https://arxiv.org/abs/2105.15203v3 | mIoU | 71.80 |
10-shot image generation > Semantic Segmentation | dacl10k v1 testdev | FPN EfficientNet-B4 w/ Aux loss | https://arxiv.org/abs/2309.00460v1 | mIoU | 0.414 |
10-shot image generation > Semantic Segmentation | dacl10k v1 testdev | DeepLabv3+ EfficientNet-B4 | https://arxiv.org/abs/2309.00460v1 | mIoU | 0.411 |
10-shot image generation > Semantic Segmentation | dacl10k v1 testdev | SegFormer mit-b1 | https://arxiv.org/abs/2309.00460v1 | mIoU | 0.40 |
10-shot image generation > Semantic Segmentation | DELIVER | CAFuser | https://arxiv.org/abs/2410.10791v2 | mIoU | 67.8 |
10-shot image generation > Semantic Segmentation | DELIVER | CAFuser | https://arxiv.org/abs/2410.10791v2 | test mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | DELIVER | GeminiFusion | https://arxiv.org/abs/2406.01210v2 | mIoU | 66.9 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-D-E-LiDAR) | https://arxiv.org/abs/2303.01480v1 | mIoU | 66.30 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-D-LiDAR) | https://arxiv.org/abs/2303.01480v1 | mIoU | 65.50 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-D-Event) | https://arxiv.org/abs/2303.01480v1 | mIoU | 64.44 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-Depth) | https://arxiv.org/abs/2303.01480v1 | mIoU | 63.58 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-LiDAR) | https://arxiv.org/abs/2303.01480v1 | mIoU | 58.04 |
10-shot image generation > Semantic Segmentation | DELIVER | CMNeXt (RGB-Event) | https://arxiv.org/abs/2303.01480v1 | mIoU | 57.48 |
10-shot image generation > Semantic Segmentation | DELIVER | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU | 57.20 |
10-shot image generation > Semantic Segmentation | SWINySEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Precision | 0.959 |
10-shot image generation > Semantic Segmentation | SWINySEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Recall | 0.979 |
10-shot image generation > Semantic Segmentation | SWINySEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | F1-Score | 0.968 |
10-shot image generation > Semantic Segmentation | SWINySEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Mean IoU | 0.993 |
10-shot image generation > Semantic Segmentation | SWINySEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | MCC | 0.960 |
10-shot image generation > Semantic Segmentation | UPLight | ShareCMP (B2 RGB-FP) | https://arxiv.org/abs/2312.03430v2 | mIoU | 92.45 |
10-shot image generation > Semantic Segmentation | UPLight | CMX (B2 RGB-AoLP) | https://arxiv.org/abs/2203.04838v5 | mIoU | 92.13 |
10-shot image generation > Semantic Segmentation | UPLight | CMX (B2 RGB-DoLP) | https://arxiv.org/abs/2203.04838v5 | mIoU | 92.07 |
10-shot image generation > Semantic Segmentation | UPLight | SegFormer-B2 (RGB) | https://arxiv.org/abs/2105.15203v3 | mIoU | 89.60 |
10-shot image generation > Semantic Segmentation | UPLight | EAFNet (RGB-AoLP) | https://arxiv.org/abs/2011.13313v2 | mIoU | 87.53 |
10-shot image generation > Semantic Segmentation | UPLight | EAFNet (RGB-DoLP) | https://arxiv.org/abs/2011.13313v2 | mIoU | 87.34 |
10-shot image generation > Semantic Segmentation | UPLight | MCubeSNet (RGB-AoLP) | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_Multimodal_Material_Segmentation_CVPR_2022_paper.html | mIoU | 82.64 |
10-shot image generation > Semantic Segmentation | UPLight | MCubeSNet (RGB-DoLP) | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_Multimodal_Material_Segmentation_CVPR_2022_paper.html | mIoU | 80.80 |
10-shot image generation > Semantic Segmentation | AIRS | ICT-Net | https://arxiv.org/abs/1912.09216v1 | IoU | 91.7 |
10-shot image generation > Semantic Segmentation | Cityscapes test | VLTSeg | https://arxiv.org/abs/2312.02021v4 | Mean IoU (class) | 86.4 |
10-shot image generation > Semantic Segmentation | Cityscapes test | MetaPrompt-SD | https://arxiv.org/abs/2312.14733v1 | Mean IoU (class) | 86.2 |
10-shot image generation > Semantic Segmentation | Cityscapes test | InternImage-H | https://arxiv.org/abs/2211.05778v4 | Mean IoU (class) | 86.1% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HS3-Fuse | https://arxiv.org/abs/2111.02333v1 | Mean IoU (class) | 85.8% |
10-shot image generation > Semantic Segmentation | Cityscapes test | InverseForm | https://arxiv.org/abs/2104.02745v2 | Mean IoU (class) | 85.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | ViT-Adapter-L (Mask2Former, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | Mean IoU (class) | 85.2% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SERNet-Former | https://arxiv.org/abs/2401.15741v7 | Mean IoU (class) | 84.83 |
10-shot image generation > Semantic Segmentation | Cityscapes test | Depth Anything | https://arxiv.org/abs/2401.10891v2 | Mean IoU (class) | 84.8% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HRNetV2 + OCR + | https://arxiv.org/abs/1909.11065v6 | Mean IoU (class) | 84.5% |
10-shot image generation > Semantic Segmentation | Cityscapes test | EfficientPS | https://arxiv.org/abs/2004.02307v3 | Mean IoU (class) | 84.21% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Panoptic-DeepLab | https://arxiv.org/abs/1911.10194v3 | Mean IoU (class) | 84.2% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HRNetV2 + OCR (w/ ASP) | https://arxiv.org/abs/1909.11065v6 | Mean IoU (class) | 83.7% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DCNAS(coarse + Mapillary) | https://arxiv.org/abs/2003.11883v2 | Mean IoU (class) | 83.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Euclidean Frank-Wolfe CRFs (backbone: DeepLabv3+)(coarse) | https://arxiv.org/abs/2110.14759v2 | Mean IoU (class) | 83.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | GALDNet(+Mapillary)++ | https://arxiv.org/abs/1909.07229v1 | Mean IoU (class) | 83.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | ResNeSt200 (Mapillary) | https://arxiv.org/abs/2004.08955v2 | Mean IoU (class) | 83.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HANet (Height-driven Attention Networks by LGE A&B)(coarse) | https://arxiv.org/abs/2003.05128v3 | Mean IoU (class) | 83.2% |
10-shot image generation > Semantic Segmentation | Cityscapes test | kMaX-DeepLab (ConvNeXt-L, fine only) | https://arxiv.org/abs/2207.04044v5 | Mean IoU (class) | 83.2% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SegFormer (MiT-B5, Mapillary) | https://arxiv.org/abs/2105.15203v3 | Mean IoU (class) | 83.1% |
10-shot image generation > Semantic Segmentation | Cityscapes test | OCR (HRNetV2-W48, coarse) | https://arxiv.org/abs/1909.11065v6 | Mean IoU (class) | 83.0% |
10-shot image generation > Semantic Segmentation | Cityscapes test | MRFM(coarse) | https://arxiv.org/abs/2011.08577v2 | Mean IoU (class) | 83.0% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DNL (coarse) | https://arxiv.org/abs/2006.06668v2 | Mean IoU (class) | 83% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DRAN(ResNet-101) WITH ONLY FINE ANNOTATED DATA | https://ieeexplore.ieee.org/document/9154612 | Mean IoU (class) | 82.9% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Gated-SCNN | https://arxiv.org/abs/1907.05740v1 | Mean IoU (class) | 82.8% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Dense Prediction Cell | http://arxiv.org/abs/1809.04184v1 | Mean IoU (class) | 82.7% |
10-shot image generation > Semantic Segmentation | Cityscapes test | CAA (ResNet-101) | https://arxiv.org/abs/2101.07434v5 | Mean IoU (class) | 82.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | OCR (ResNet-101, coarse) | https://arxiv.org/abs/1909.11065v6 | Mean IoU (class) | 82.4% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DDRNet-39 1.5x | https://arxiv.org/abs/2101.06085v2 | Mean IoU (class) | 82.4% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SSMA | https://arxiv.org/abs/1808.03833v3 | Mean IoU (class) | 82.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Gated Fully Fusion | https://arxiv.org/abs/1904.01803v2 | Mean IoU (class) | 82.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Auto-DeepLab-L | http://arxiv.org/abs/1901.02985v2 | Mean IoU (class) | 82.1% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DGCNet (ResNet-101) | https://arxiv.org/abs/1909.06121v3 | Mean IoU (class) | 82% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SPNet (ResNet-101) | https://arxiv.org/abs/2003.13328v1 | Mean IoU (class) | 82.0% |
10-shot image generation > Semantic Segmentation | Cityscapes test | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | Mean IoU (class) | 81.8% |
10-shot image generation > Semantic Segmentation | Cityscapes test | RPCNet | https://arxiv.org/abs/2004.07684v1 | Mean IoU (class) | 81.8 |
10-shot image generation > Semantic Segmentation | Cityscapes test | OCNet | https://arxiv.org/abs/1809.00916v4 | Mean IoU (class) | 81.7% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SETR-PUP++ | https://arxiv.org/abs/2012.15840v3 | Mean IoU (class) | 81.64% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HRNet (HRNetV2-W48) | http://arxiv.org/abs/1904.04514v1 | Mean IoU (class) | 81.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | HRNetV2 (train+val) | https://arxiv.org/abs/1908.07919v2 | Mean IoU (class) | 81.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DANet (ResNet-101) | http://arxiv.org/abs/1809.02983v4 | Mean IoU (class) | 81.5% |
10-shot image generation > Semantic Segmentation | Cityscapes test | CCNet | https://arxiv.org/abs/1811.11721v2 | Mean IoU (class) | 81.4% |
10-shot image generation > Semantic Segmentation | Cityscapes test | BFP | https://arxiv.org/abs/1909.00179v1 | Mean IoU (class) | 81.4% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DeepLabv3 (ResNet-101, coarse) | http://arxiv.org/abs/1706.05587v3 | Mean IoU (class) | 81.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | CPN(ResNet-101) | https://arxiv.org/abs/2004.01547v1 | Mean IoU (class) | 81.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Asymmetric ALNN | https://arxiv.org/abs/1908.07678v5 | Mean IoU (class) | 81.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | AdapNet++ | https://arxiv.org/abs/1808.03833v3 | Mean IoU (class) | 81.24% |
10-shot image generation > Semantic Segmentation | Cityscapes test | SVCNet (ResNet-101) | https://arxiv.org/abs/1909.02651v1 | Mean IoU (class) | 81.0% |
10-shot image generation > Semantic Segmentation | Cityscapes test | D3Net-L | https://arxiv.org/abs/2011.11844v2 | Mean IoU (class) | 80.8% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DenseASPP (DenseNet-161) | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.html | Mean IoU (class) | 80.6% |
10-shot image generation > Semantic Segmentation | Cityscapes test | Smooth Network with Channel Attention Block | http://arxiv.org/abs/1804.09337v1 | Mean IoU (class) | 80.3% |
10-shot image generation > Semantic Segmentation | Cityscapes test | PSPNet++ | http://arxiv.org/abs/1612.01105v2 | Mean IoU (class) | 80.2% |
10-shot image generation > Semantic Segmentation | Cityscapes test | PSANet (ResNet-101) | http://openaccess.thecvf.com/content_ECCV_2018/html/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.html | Mean IoU (class) | 80.1% |
10-shot image generation > Semantic Segmentation | Cityscapes test | ESANet-R34-NBt1D | https://arxiv.org/abs/2011.06961v3 | Mean IoU (class) | 80.09% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DeepLabV3 with R-101 | https://arxiv.org/abs/2307.14179v1 | Mean IoU (class) | 79.9% |
10-shot image generation > Semantic Segmentation | Cityscapes test | DFN (ResNet-101) | http://arxiv.org/abs/1804.09337v1 | Mean IoU (class) | 79.3% |
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