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 > 3D Semantic Segmentation | SensatUrban | EyeNet | https://arxiv.org/abs/2301.12972v3 | mIoU | 62.30 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | EyeNet | https://arxiv.org/abs/2301.12972v3 | oAcc | 93.7 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | BEV-Seg3D-Net | https://arxiv.org/abs/2109.09074v1 | mIoU | 61.7 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | KPConv | https://arxiv.org/abs/1904.08889v2 | mIoU | 57.58 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | 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 | 55.1 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | SparseConv | http://arxiv.org/abs/1711.10275v1 | mIoU | 42.66 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | SPGraph | http://arxiv.org/abs/1711.09869v2 | mIoU | 37.29 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | SensatUrban | TangentConv | http://arxiv.org/abs/1807.02443v1 | mIoU | 33.30 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | WOD-C | MinkUNet-34 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 96.21% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | WOD-C | SPVCNN-34 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 98.72% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | WOD-C | MinkUNet-18 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 100.00% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | WOD-C | SPVCNN-18 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 103.60% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | WOD-C | Cylinder3D (torchsparse) | https://arxiv.org/abs/2011.10033v1 | mean Corruption Error (mCE) | 106.02% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | SPVCNN-34 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 99.16% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | KPConv | https://arxiv.org/abs/1904.08889v2 | mean Corruption Error (mCE) | 99.54% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | MinkUNet-18 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 100.00% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | SPVCNN-18 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 100.30% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | MinkUNet-34 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 100.61% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | PIDS-2.0x | https://arxiv.org/abs/2211.15759v2 | mean Corruption Error (mCE) | 101.20% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | Cylinder3D (torchsparse) | https://arxiv.org/abs/2011.10033v1 | mean Corruption Error (mCE) | 103.13% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | Cylinder3D (spconv) | https://arxiv.org/abs/2011.10033v1 | mean Corruption Error (mCE) | 103.25% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | CENet (64x2048) | https://arxiv.org/abs/2207.12691v1 | mean Corruption Error (mCE) | 103.41% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | PIDS-1.2x | https://arxiv.org/abs/2211.15759v2 | mean Corruption Error (mCE) | 104.13% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | 2DPASS | https://arxiv.org/abs/2207.04397v3 | mean Corruption Error (mCE) | 106.14% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | CPGNet | https://arxiv.org/abs/2204.09914v3 | mean Corruption Error (mCE) | 107.34% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | GFNet | https://arxiv.org/abs/2207.02605v2 | mean Corruption Error (mCE) | 108.68% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | WaffleIron | https://arxiv.org/abs/2301.10100v2 | mean Corruption Error (mCE) | 109.54% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | RPVNet | https://arxiv.org/abs/2103.12978v1 | mean Corruption Error (mCE) | 111.74% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | FIDNet (64x2048) | https://arxiv.org/abs/2109.03787v1 | mean Corruption Error (mCE) | 113.81% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | SalsaNext (64x2048) | https://arxiv.org/abs/2003.03653v4 | mean Corruption Error (mCE) | 116.14% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | PolarNet | https://arxiv.org/abs/2003.14032v2 | mean Corruption Error (mCE) | 118.56% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | RangeNet-53 (64x2048) | http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf | mean Corruption Error (mCE) | 130.66% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | RangeNet-21 (64x2048) | http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf | mean Corruption Error (mCE) | 136.33% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | SqueezeSegV2 (64x2048) | http://arxiv.org/abs/1809.08495v1 | mean Corruption Error (mCE) | 152.45% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | SemanticKITTI-C | SqueezeSeg (64x2048) | http://arxiv.org/abs/1710.07368v1 | mean Corruption Error (mCE) | 164.87% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | GFNet | https://arxiv.org/abs/2207.02605v2 | mean Corruption Error (mCE) | 92.55% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | MinkUNet-34 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 96.37% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | SPVCNN-34 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 97.45% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | 2DPASS | https://arxiv.org/abs/2207.04397v3 | mean Corruption Error (mCE) | 98.56% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | MinkUNet-18 | https://arxiv.org/abs/1904.08755v4 | mean Corruption Error (mCE) | 100.00% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | Cylinder3D (torchsparse) | https://arxiv.org/abs/2011.10033v1 | mean Corruption Error (mCE) | 105.56% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | SPVCNN-18 | https://arxiv.org/abs/2007.16100v2 | mean Corruption Error (mCE) | 106.65% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | WaffleIron | https://arxiv.org/abs/2301.10100v2 | mean Corruption Error (mCE) | 106.73% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | Cylinder3D (spconv) | https://arxiv.org/abs/2011.10033v1 | mean Corruption Error (mCE) | 111.84% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | CENet | https://arxiv.org/abs/2207.12691v1 | mean Corruption Error (mCE) | 112.79% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | PolarNet | https://arxiv.org/abs/2003.14032v2 | mean Corruption Error (mCE) | 115.09% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Robust 3D Semantic Segmentation | nuScenes-C | FIDNet | https://arxiv.org/abs/2109.03787v1 | mean Corruption Error (mCE) | 122.42% |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet-tiny | https://arxiv.org/abs/2002.10893v5 | Speed (FPS) | 98 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet-tiny | https://arxiv.org/abs/2002.10893v5 | mIoU | 46.9 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet-tiny | https://arxiv.org/abs/2002.10893v5 | Parameters (M) | 0.44 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | MINet | https://arxiv.org/abs/2008.09162v2 | Speed (FPS) | 47 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | MINet | https://arxiv.org/abs/2008.09162v2 | mIoU | 55.2 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | MINet | https://arxiv.org/abs/2008.09162v2 | Parameters (M) | 1.0 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet | https://arxiv.org/abs/2002.10893v5 | Speed (FPS) | 28 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet | https://arxiv.org/abs/2002.10893v5 | mIoU | 55.8 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | 3D-MiniNet | https://arxiv.org/abs/2002.10893v5 | Parameters (M) | 3.97 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Real-Time 3D Semantic Segmentation | SemanticKITTI | MPF | https://arxiv.org/abs/2011.01974v2 | Speed (FPS) | 20.6 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Unsupervised 3D Semantic Segmentation | ScanNetV2 | Point-GCC+PointNet++ | https://arxiv.org/abs/2305.19623v2 | mIoU | 18.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation > Unsupervised 3D Semantic Segmentation | ScanNetV2 | SL3D | https://arxiv.org/abs/2210.16810v3 | mIoU | 10.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | DHR (Swin-L, Mask2Former) | https://arxiv.org/abs/2404.00380v2 | mIoU | 56.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | SemPLeS (Swin-L) | https://arxiv.org/abs/2401.11791v4 | mIoU | 56.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | WSSS-SAM(DeepLabV2-ResNet101) | https://arxiv.org/abs/2305.01586v2 | mIoU | 55.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | FMA-WSSS (Swin-L) | https://arxiv.org/abs/2312.03585v2 | mIoU | 55.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | CoSA (SWIN-B, multi-stage) | https://arxiv.org/abs/2402.17891v2 | mIoU | 53.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | CoSA (ViT-B, single-stage) | https://arxiv.org/abs/2402.17891v2 | mIoU | 51.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | WeakTr (ViT-S, multi-stage) | https://arxiv.org/abs/2304.01184v2 | mIoU | 50.3 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | MARS (ResNet-101, multi-stage) | https://arxiv.org/abs/2304.09913v1 | mIoU | 49.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | WeakTr (DeiT-S, multi-stage) | https://arxiv.org/abs/2304.01184v2 | mIoU | 46.9 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | RS+EPM (ResNet-101, multi-stage) | https://arxiv.org/abs/2204.06754v4 | mIoU | 46.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | T2MDiffusion(DeepLabV2-ResNet101) | https://arxiv.org/abs/2309.04109v2 | mIoU | 45.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | FBR | https://arxiv.org/abs/2406.15755v1 | mIoU | 45.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | CLIP-ES(DeepLabV2-ResNet101) | https://arxiv.org/abs/2212.09506v3 | mIoU | 45.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | ACR(DeeplabV1-ResNet38) | http://openaccess.thecvf.com//content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html | mIoU | 45.3 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | BECO(DeepLabV3Plus+R101) | http://openaccess.thecvf.com//content/CVPR2023/html/Rong_Boundary-Enhanced_Co-Training_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2023_paper.html | mIoU | 45.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | ViT-PCM | https://arxiv.org/abs/2210.17400v1 | mIoU | 45.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | ACR-WSSS(DeepLabV2-ResNet101) | https://arxiv.org/abs/2308.04321v2 | mIoU | 45.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | AMN (DeepLabV2-ResNet101) | https://arxiv.org/abs/2203.16045v1 | mIoU | 44.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | L2G (DeepLabV2-ResNet101) | https://arxiv.org/abs/2204.03206v1 | mIoU | 44.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | RIB (DeepLabV2-ResNet101, No Saliency) | https://arxiv.org/abs/2110.06530v1 | mIoU | 43.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | SIPE(ResNet-38, no saliency, no RW) | https://arxiv.org/abs/2203.02909v1 | mIoU | 43.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | RS+EPM (ResNet-50, single-stage) | https://arxiv.org/abs/2204.06754v4 | mIoU | 42.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | ClusterCAM | https://ieeexplore.ieee.org/abstract/document/10381698 | mIoU | 41.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | URN(Res2Net-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | mIoU | 41.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | URN(ScaleNet-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | mIoU | 40.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | URN(ResNet-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | mIoU | 40.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | URN(ResNet-38, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | mIoU | 40.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | PMM(ScaleNet101, no saliency, no RW) | https://arxiv.org/abs/2108.12995v2 | mIoU | 40.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | AFA | https://arxiv.org/abs/2203.02664v2 | mIoU | 38.9 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | PMM(ResNet38, no saliency, no RW) | https://arxiv.org/abs/2108.12995v2 | mIoU | 36.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | OC-CSE(ResNet38, no saliency, no RW) | http://openaccess.thecvf.com//content/ICCV2021/html/Kweon_Unlocking_the_Potential_of_Ordinary_Classifier_Class-Specific_Adversarial_Erasing_Framework_ICCV_2021_paper.html | mIoU | 36.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | VWL-L | https://arxiv.org/abs/2202.04812v1 | mIoU | 36.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | VWL-M | https://arxiv.org/abs/2202.04812v1 | mIoU | 36.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | EPS | https://arxiv.org/abs/2105.08965v1 | mIoU | 35.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | SLRNet | https://arxiv.org/abs/2203.10278v1 | mIoU | 35.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | SGAN | https://arxiv.org/abs/1910.05475v2 | mIoU | 33.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | IRNet+CONTA | https://arxiv.org/abs/2009.12547v2 | mIoU | 33.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | GroupWSSS | https://arxiv.org/abs/2012.05007v1 | mIoU | 28.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO 2014 val | DSRG | http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.html | mIoU | 26.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | COCO-Stuff val | DHR (Swin-L, Mask2Former) | https://arxiv.org/abs/2404.00380v2 | mIoU | 37.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 train | FMA-WSSS | https://arxiv.org/abs/2312.03585v2 | Mean IoU | 80.4 |
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