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 | ISPRS Potsdam | EfficientUNets and Transformers | https://arxiv.org/abs/2206.09731v2 | Overall Accuracy | 91.8 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | EfficientUNets and Transformers | https://arxiv.org/abs/2206.09731v2 | Mean F1 | 93.7 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | IMP-ViTAEv2-S-UperNet | https://arxiv.org/abs/2204.02825v4 | Overall Accuracy | 91.6 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | MANet | https://ieeexplore.ieee.org/abstract/document/9487010 | Overall Accuracy | 91.318 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Overall Accuracy | 91.3 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Mean F1 | 92.8 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Mean IoU | 86.8 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | ABCNet | https://arxiv.org/abs/2102.02531v1 | Overall Accuracy | 91.3 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | ViTAE-B + RVSA -UperNet | https://arxiv.org/abs/2208.03987v4 | Overall Accuracy | 91.22 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | RSP-ViTAEv2-S-UperNet | https://arxiv.org/abs/2204.02825v4 | Overall Accuracy | 91.21 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | BANet | https://arxiv.org/abs/2106.12413v2 | Overall Accuracy | 91.06 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | RSP-Swin-T-UperNet | https://arxiv.org/abs/2204.02825v4 | Overall Accuracy | 90.78 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | ViT-B + RVSA-UperNet | https://arxiv.org/abs/2208.03987v4 | Overall Accuracy | 90.77 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | RSP-ResNet-50-UperNet | https://arxiv.org/abs/2204.02825v4 | Overall Accuracy | 90.61 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | PSPNet (SAP) | https://arxiv.org/abs/2409.16630v1 | Overall Accuracy | 88.56 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | PSPNet (SAP) | https://arxiv.org/abs/2409.16630v1 | Mean IoU | 74.3 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | D2LS | https://arxiv.org/abs/2503.06683v1 | Mean F1 | 94.7 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | SFA-Net | https://www.mdpi.com/2072-4292/16/17/3278 | Mean F1 | 93.5 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | U-Net (ConvFormer-M36) | https://www.mdpi.com/2072-4292/16/12/2077 | Mean IoU | 89.45 |
10-shot image generation > Semantic Segmentation | Vaihingen | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 82.87 |
10-shot image generation > Semantic Segmentation | Vaihingen | LMFNet-2 ( | https://arxiv.org/abs/2404.13659v1 | mIoU | 82.49 |
10-shot image generation > Semantic Segmentation | Vaihingen | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU | 81.03 |
10-shot image generation > Semantic Segmentation | Vaihingen | V-FuseNet | http://arxiv.org/abs/1711.08681v1 | mIoU | 79.56 |
10-shot image generation > Semantic Segmentation | Vaihingen | UnetFormer | https://arxiv.org/abs/2109.08937v4 | mIoU | 77.24 |
10-shot image generation > Semantic Segmentation | Vaihingen | SegFormer-B1 | https://arxiv.org/abs/2105.15203v3 | mIoU | 76.92 |
10-shot image generation > Semantic Segmentation | Vaihingen | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 76.79 |
10-shot image generation > Semantic Segmentation | Vaihingen | HRNet-48 | https://arxiv.org/abs/1908.07919v2 | mIoU | 76.75 |
10-shot image generation > Semantic Segmentation | Vaihingen | SegFormer-B2 | https://arxiv.org/abs/2105.15203v3 | mIoU | 76.69 |
10-shot image generation > Semantic Segmentation | Vaihingen | HRNet-18 | https://arxiv.org/abs/1908.07919v2 | mIoU | 75.90 |
10-shot image generation > Semantic Segmentation | Vaihingen | SegFormer-B0 | https://arxiv.org/abs/2105.15203v3 | mIoU | 75.57 |
10-shot image generation > Semantic Segmentation | Vaihingen | FPN | http://arxiv.org/abs/1612.03144v2 | mIoU | 74.86 |
10-shot image generation > Semantic Segmentation | Vaihingen | DeepLabV3+ | http://arxiv.org/abs/1802.02611v3 | mIoU | 72.90 |
10-shot image generation > Semantic Segmentation | BJRoad | CMNeXt | https://arxiv.org/abs/2303.01480v1 | IoU | 63.22 |
10-shot image generation > Semantic Segmentation | BJRoad | CMMPNe | https://arxiv.org/abs/2111.15119v3 | IoU | 62.85 |
10-shot image generation > Semantic Segmentation | BJRoad | CMX | https://arxiv.org/abs/2203.04838v5 | IoU | 62.28 |
10-shot image generation > Semantic Segmentation | BJRoad | SA-Gate | https://arxiv.org/abs/2007.09183v1 | IoU | 62.14 |
10-shot image generation > Semantic Segmentation | BJRoad | DeepDualMapper | https://arxiv.org/abs/2002.06832v1 | IoU | 60.19 |
10-shot image generation > Semantic Segmentation | BJRoad | Sun et al. | https://arxiv.org/abs/1905.01447v1 | IoU | 59.18 |
10-shot image generation > Semantic Segmentation | BJRoad | D-LinkNet | https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf | IoU | 57.96 |
10-shot image generation > Semantic Segmentation | BJRoad | LinkNet | http://arxiv.org/abs/1707.03718v1 | IoU | 57.89 |
10-shot image generation > Semantic Segmentation | BJRoad | UNet | http://arxiv.org/abs/1505.04597v1 | IoU | 54.88 |
10-shot image generation > Semantic Segmentation | BJRoad | Res-UNet | http://arxiv.org/abs/1711.10684v1 | IoU | 54.24 |
10-shot image generation > Semantic Segmentation | BJRoad | DeepLabv3+ | http://arxiv.org/abs/1802.02611v3 | IoU | 50.81 |
10-shot image generation > Semantic Segmentation | RELLIS-3D Dataset | Swiftnet | https://doi.org/10.4271/2023-01-0868 | Mean IoU (class) | 77.9 |
10-shot image generation > Semantic Segmentation | RELLIS-3D Dataset | GA-Nav | https://arxiv.org/abs/2103.04233v5 | Mean IoU (class) | 74.44 |
10-shot image generation > Semantic Segmentation | RELLIS-3D Dataset | gscnn | https://arxiv.org/abs/2011.12954v4 | Mean IoU (class) | 50.13 |
10-shot image generation > Semantic Segmentation | RELLIS-3D Dataset | hrnet+OCR | https://arxiv.org/abs/2011.12954v4 | Mean IoU (class) | 48.83 |
10-shot image generation > Semantic Segmentation | RELLIS-3D Dataset | DeeplabV3 + Resnet | https://doi.org/10.4271/2023-01-0740 | Mean IoU (class) | 35.882 |
10-shot image generation > Semantic Segmentation | SkyScapes-Lane | SkyScapesNet-Lane | http://openaccess.thecvf.com/content_ICCV_2019/html/Azimi_SkyScapes__Fine-Grained_Semantic_Understanding_of_Aerial_Scenes_ICCV_2019_paper.html | Mean IoU | 50.93 |
10-shot image generation > Semantic Segmentation | SkyScapes-Lane | FCN8s (ResNet-50) | http://arxiv.org/abs/1411.4038v2 | Mean IoU | 13.74 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2007 | GALDNet | https://arxiv.org/abs/1909.07229v1 | Mean IoU | 83 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2007 | DeepLabv3 (ImageNet+300M) | http://arxiv.org/abs/1707.02968v2 | Mean IoU | 81.3 |
10-shot image generation > Semantic Segmentation | Cam2BEV | uNetXST | https://arxiv.org/abs/2005.04078v1 | Mean IoU | 71.92 |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (M) | https://arxiv.org/abs/2107.03172v2 | mIoU | 75.14% |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (M) | https://arxiv.org/abs/2107.03172v2 | GFLOPs | 34.38 |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (S) | https://arxiv.org/abs/2107.03172v2 | mIoU | 74.15% |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (S) | https://arxiv.org/abs/2107.03172v2 | GFLOPs | 19.92 |
10-shot image generation > Semantic Segmentation | Trans10K | Trans2Seg | https://arxiv.org/abs/2101.08461v3 | mIoU | 72.15% |
10-shot image generation > Semantic Segmentation | Trans10K | Trans2Seg | https://arxiv.org/abs/2101.08461v3 | GFLOPs | 49.03 |
10-shot image generation > Semantic Segmentation | Trans10K | Trans2Lab | https://arxiv.org/abs/2003.13948v3 | mIoU | 69.00% |
10-shot image generation > Semantic Segmentation | Trans10K | Trans2Lab | https://arxiv.org/abs/2003.13948v3 | GFLOPs | 61.31 |
10-shot image generation > Semantic Segmentation | Trans10K | DeepLabV3+ | http://arxiv.org/abs/1802.02611v3 | mIoU | 68.87% |
10-shot image generation > Semantic Segmentation | Trans10K | DeepLabV3+ | http://arxiv.org/abs/1802.02611v3 | GFLOPs | 37.98 |
10-shot image generation > Semantic Segmentation | Trans10K | DANet | http://arxiv.org/abs/1809.02983v4 | mIoU | 68.81% |
10-shot image generation > Semantic Segmentation | Trans10K | DANet | http://arxiv.org/abs/1809.02983v4 | GFLOPs | 198.00 |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (T) | https://arxiv.org/abs/2107.03172v2 | mIoU | 68.63% |
10-shot image generation > Semantic Segmentation | Trans10K | Trans4Trans (T) | https://arxiv.org/abs/2107.03172v2 | GFLOPs | 10.45 |
10-shot image generation > Semantic Segmentation | Trans10K | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 68.23% |
10-shot image generation > Semantic Segmentation | Trans10K | PSPNet | http://arxiv.org/abs/1612.01105v2 | GFLOPs | 187.03 |
10-shot image generation > Semantic Segmentation | Trans10K | OCNet | https://arxiv.org/abs/1809.00916v4 | mIoU | 66.31% |
10-shot image generation > Semantic Segmentation | Trans10K | OCNet | https://arxiv.org/abs/1809.00916v4 | GFLOPs | 43.31 |
10-shot image generation > Semantic Segmentation | Trans10K | DenseASPP | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.html | mIoU | 63.01% |
10-shot image generation > Semantic Segmentation | Trans10K | DenseASPP | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.html | GFLOPs | 36.20 |
10-shot image generation > Semantic Segmentation | Trans10K | FCN | http://arxiv.org/abs/1411.4038v2 | mIoU | 62.75% |
10-shot image generation > Semantic Segmentation | Trans10K | FCN | http://arxiv.org/abs/1411.4038v2 | GFLOPs | 42.23 |
10-shot image generation > Semantic Segmentation | Trans10K | BiSeNet | http://arxiv.org/abs/1808.00897v1 | mIoU | 58.40% |
10-shot image generation > Semantic Segmentation | Trans10K | BiSeNet | http://arxiv.org/abs/1808.00897v1 | GFLOPs | 19.91 |
10-shot image generation > Semantic Segmentation | Trans10K | RefineNet | http://arxiv.org/abs/1611.06612v3 | mIoU | 58.18% |
10-shot image generation > Semantic Segmentation | Trans10K | RefineNet | http://arxiv.org/abs/1611.06612v3 | GFLOPs | 44.56 |
10-shot image generation > Semantic Segmentation | Trans10K | ICNet | http://arxiv.org/abs/1704.08545v2 | mIoU | 23.39% |
10-shot image generation > Semantic Segmentation | Trans10K | ICNet | http://arxiv.org/abs/1704.08545v2 | GFLOPs | 10.64 |
10-shot image generation > Semantic Segmentation | Trans10K | U-Net | http://arxiv.org/abs/1505.04597v1 | GFLOPs | 124.55 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2010 test | SIW | https://arxiv.org/abs/2202.02002v2 | Mean IoU | 81.1 |
10-shot image generation > Semantic Segmentation | COCO-Stuff | Deeplab v2 | http://arxiv.org/abs/1612.03716v4 | mIoU | 33.2 |
10-shot image generation > Semantic Segmentation | COCO-Stuff | Deeplab v2 | http://arxiv.org/abs/1612.03716v4 | Per-Class Accuracy | 45.1 |
10-shot image generation > Semantic Segmentation | COCO-Stuff | Deeplab v2 | http://arxiv.org/abs/1612.03716v4 | Pixel Accuracy | 63.6 |
10-shot image generation > Semantic Segmentation | COCO-Stuff | Deeplab v2 | http://arxiv.org/abs/1612.03716v4 | F.W. IU | 47.6 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUROC | 0.983 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUPRC | 0.94 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | F1 | 0.939 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUROC | 0.944 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUPRC | 0.92 |
10-shot image generation > Semantic Segmentation | HERA RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | F1 | 0.953 |
10-shot image generation > Semantic Segmentation | UAVid | U-Net Ensemble | https://www.mdpi.com/2072-4292/16/12/2077 | Mean IoU | 73.34 |
10-shot image generation > Semantic Segmentation | UAVid | U-Net (MaxViT-S) | https://www.mdpi.com/2072-4292/16/12/2077 | Mean IoU | 71.88 |
10-shot image generation > Semantic Segmentation | UAVid | D2LS | https://arxiv.org/abs/2503.06683v1 | Mean IoU | 70.9 |
10-shot image generation > Semantic Segmentation | UAVid | SFA-Net | https://www.mdpi.com/2072-4292/16/17/3278 | Mean IoU | 70.4 |
10-shot image generation > Semantic Segmentation | UAVid | LSKNet-S | https://arxiv.org/abs/2403.11735v5 | Mean IoU | 70.0 |
10-shot image generation > Semantic Segmentation | UAVid | LSKNet-T | https://arxiv.org/abs/2403.11735v5 | Mean IoU | 69.3 |
10-shot image generation > Semantic Segmentation | UAVid | LWGANet L2 | https://arxiv.org/abs/2501.10040v1 | Mean IoU | 69.1 |
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