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 > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | GETAM(vitb-hybrid) | https://arxiv.org/abs/2112.02841v2 | Mean IoU | 72.3 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | Puzzle-CAM (ResNeSt-269) | https://arxiv.org/abs/2101.11253v4 | Mean IoU | 72.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ADELE (DeepLabV1-ResNet38) | https://arxiv.org/abs/2110.03740v2 | Mean IoU | 72.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | EPS(DeepLabV1-ResNet101 | https://arxiv.org/abs/2105.08965v1 | Mean IoU | 71.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | Infer-CAM(DeepLabV2-R101) | https://arxiv.org/abs/2110.14309v1 | Mean IoU | 71.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | SPML (DeepLabV2-R101) | https://arxiv.org/abs/2105.00957v2 | Mean IoU | 71.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | URN(Res2Net-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | Mean IoU | 71.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ISIM (ResNet-101) | https://arxiv.org/abs/2211.12455v2 | Mean IoU | 71.45 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | VWL-L (EMANet) | https://arxiv.org/abs/2202.04812v1 | Mean IoU | 71.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ViT-PCM | https://arxiv.org/abs/2210.17400v1 | Mean IoU | 70.9 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ACR-WSSS(DeepLabV2-ResNet101) | https://arxiv.org/abs/2308.04321v2 | Mean IoU | 70.9 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | EPS(DeepLabV2-ResNet101) | https://arxiv.org/abs/2105.08965v1 | Mean IoU | 70.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | URN(ScaleNet-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | Mean IoU | 70.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ICAM | https://arxiv.org/abs/2203.12459v3 | Mean IoU | 70.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | DRS (DeepLabV2-R101) | https://arxiv.org/abs/2103.07246v2 | Mean IoU | 70.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | VWL-L | https://arxiv.org/abs/2202.04812v1 | Mean IoU | 70.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | ClusterCAM | https://ieeexplore.ieee.org/abstract/document/10381698 | Mean IoU | 70.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | URN(ResNet-38, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | Mean IoU | 70.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | AMN (DeepLabV2-ResNet101, MS-COCO-pretrained weights) | https://arxiv.org/abs/2203.16045v1 | Mean IoU | 70.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | RS+EPM (ResNet-50, single-stage) | https://arxiv.org/abs/2204.06754v4 | Mean IoU | 70.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | PMM(Res2Net101, no saliency, no RW) | https://arxiv.org/abs/2108.12995v2 | Mean IoU | 70.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | VWL-M | https://arxiv.org/abs/2202.04812v1 | Mean IoU | 70.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | W-OoD (WResNet-38) | https://arxiv.org/abs/2203.03860v1 | Mean IoU | 70.1 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | RIB+Sal (DeepLabV2-ResNet101) | https://arxiv.org/abs/2110.06530v1 | Mean IoU | 70.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | URN(ResNet-101, no saliency, no RW) | https://arxiv.org/abs/2112.07431v1 | Mean IoU | 69.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | SIPE (DeepLabV2-ResNet101, no saliency) | https://arxiv.org/abs/2203.02909v1 | Mean IoU | 69.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | AMN (DeepLabV2-ResNet101) | https://arxiv.org/abs/2203.16045v1 | Mean IoU | 69.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | SLRNet | https://arxiv.org/abs/2203.10278v1 | Mean IoU | 69.4 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | WSGCN (MS-COCO-pre-trained weights) | https://arxiv.org/abs/2103.16762v2 | Mean IoU | 69.3 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | PMM(ResNet38, no saliency, no RW) | https://arxiv.org/abs/2108.12995v2 | Mean IoU | 69.0 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | WSGCN (no Saliency map) | https://arxiv.org/abs/2103.16762v2 | Mean IoU | 68.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | RIB (DeepLabV2-ResNet101) | https://arxiv.org/abs/2110.06530v1 | Mean IoU | 68.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | GroupWSSS | https://arxiv.org/abs/2012.05007v1 | Mean IoU | 68.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | CPN | https://arxiv.org/abs/2108.03852v1 | Mean IoU | 68.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | Puzzle-CAM (ResNeSt-101) | https://arxiv.org/abs/2101.11253v4 | Mean IoU | 67.7 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | SLRNet(1-stage,ResNet38) | https://arxiv.org/abs/2203.10278v1 | Mean IoU | 67.6 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | LIID | https://ieeexplore.ieee.org/abstract/document/9193980 | Mean IoU | 67.5 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | SGAN | https://arxiv.org/abs/1910.05475v2 | Mean IoU | 67.2 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | IRNet (ResNet-50) | https://arxiv.org/abs/1904.05044v3 | Mean IoU | 64.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | EADER | https://arxiv.org/abs/2011.04626v1 | Mean IoU | 63.8 |
10-shot image generation > Semantic Segmentation > Weakly-Supervised Semantic Segmentation | ADE20K val | DHR (Swin-L, Mask2Former) | https://arxiv.org/abs/2404.00380v2 | mIoU | 32.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | ScanNet | 3DMV | http://arxiv.org/abs/1803.10409v1 | Average Accuracy | 75.0% |
10-shot image generation > Semantic Segmentation > Scene Segmentation | ScanNet | PointNet++ | http://arxiv.org/abs/1612.00593v2 | Average Accuracy | 60.2% |
10-shot image generation > Semantic Segmentation > Scene Segmentation | ScanNet | KPConv | https://arxiv.org/abs/1904.08889v2 | 3DIoU | 68.6 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | MovieNet | NeighborNet | http://openaccess.thecvf.com//content/CVPR2024/html/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.html | AP | 71.9 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | MovieNet | TranS4mer | https://arxiv.org/abs/2212.14427v2 | AP | 60.78 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | UAVid | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Category mIoU | 67.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | SUN-RGBD | 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 | 50.60 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | SUN-RGBD | Index Network | https://arxiv.org/abs/1908.09895v2 | Mean IoU | 33.48 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | SUN-RGBD | DeepLab-LargeFOV | http://arxiv.org/abs/1412.7062v4 | Mean IoU | 32.08 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | SUN-RGBD | SegNet | http://arxiv.org/abs/1511.00561v3 | Mean IoU | 31.84 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | SUN-RGBD | FCN | http://arxiv.org/abs/1605.06211v1 | Mean IoU | 27.39 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | StreetHazards | Mask2Anomaly | https://arxiv.org/abs/2307.13316v1 | Open-mIoU | 59.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | StreetHazards | LDN121-RPL | https://arxiv.org/abs/2211.14512v3 | Open-mIoU | 56.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | StreetHazards | LDN121-DenseHybrid | https://arxiv.org/abs/2207.02606v1 | Open-mIoU | 45.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation | NYU Depth v2 | Dilated FCN-2s RGB | http://arxiv.org/abs/1707.08254v3 | Mean IoU | 32.3% |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | SODA Dataset | FTNet | https://ieeexplore.ieee.org/abstract/document/9585453 | mIOU | 60.08 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | CMNeXt (B4) | https://arxiv.org/abs/2303.01480v1 | mIoU | 60.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | EAEFNet | https://arxiv.org/abs/2303.15710v1 | mIoU | 60.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | CMX (B4) | https://arxiv.org/abs/2203.04838v5 | mIoU | 56.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU | 54.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIoU | 48.5 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | Noisy RS RGB-T Dataset | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIoU | 33.1 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | KP day-night | HAPNet | https://arxiv.org/abs/2404.03527v2 | mIoU | 57.6 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | KP day-night | CRM_RGBTSeg | https://arxiv.org/abs/2303.17386v2 | mIoU | 55.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | KP day-night | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 46.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | KP day-night | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIoU | 28.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | KP day-night | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIoU | 24.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | SHIFNet | https://arxiv.org/abs/2503.02581v1 | mIoU | 89.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | HAPNet | https://arxiv.org/abs/2404.03527v2 | mIoU | 89.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | CRM_RGBTSeg | https://arxiv.org/abs/2303.17386v2 | mIoU | 88 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | Sigma-small | https://arxiv.org/abs/2404.04256v2 | mIoU | 87.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | MMSFormer | https://arxiv.org/abs/2309.04001v4 | mIoU | 87.45 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | DPLNet | https://arxiv.org/abs/2312.00360v2 | mIoU | 86.7 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | CACFNet | https://ieeexplore.ieee.org/abstract/document/10251592 | mIoU | 86.56 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | EGFNet (ConvNeXt) | https://ieeexplore.ieee.org/abstract/document/10234530 | mIoU | 85.42 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | StitchFusion (RGB-T) | https://arxiv.org/abs/2408.01343v1 | mIoU | 85.35 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | CAINet (MobileNet-V2) | https://arxiv.org/abs/2401.01624v1 | mIoU | 84.74 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | UniRGB-IR | https://arxiv.org/abs/2404.17360v2 | mIoU | 82.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | SpiderMesh | https://arxiv.org/abs/2303.08692v2 | mIoU | 82.3 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | GEBNet | https://ieeexplore.ieee.org/abstract/document/9937048 | mIoU | 81.15 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | MTANet | https://ieeexplore.ieee.org/abstract/document/9749834 | mIoU | 78.60 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | EGFNet | https://arxiv.org/abs/2112.05144v1 | mIoU | 78.51 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | CSRPNet (ResNet-50) | https://arxiv.org/abs/2308.12534v1 | mIoU | 75.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | ACNet | https://arxiv.org/abs/1905.10089v1 | mIoU | 71.81 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | PSTNet | https://arxiv.org/abs/1909.10980v1 | mIoU | 68.4 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIoU | 57.6 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIoU | 57.0 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | UNet | http://arxiv.org/abs/1505.04597v1 | mIoU | 52.8 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | PST900 | Fast-SCNN | http://arxiv.org/abs/1902.04502v1 | mIoU | 47.2 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | RGB-T-Glass-Segmentation | https://arxiv.org/abs/2204.05453v4 | MAE | 0.024 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | CMX | https://arxiv.org/abs/2203.04838v5 | MAE | 0.029 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | ESANet | https://arxiv.org/abs/2011.06961v3 | MAE | 0.040 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | EBS | https://arxiv.org/abs/2112.13528v1 | MAE | 0.040 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | CLNet | https://arxiv.org/abs/2109.07246v2 | MAE | 0.041 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | SPNet | https://arxiv.org/abs/2108.08162v2 | MAE | 0.041 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | VST | https://arxiv.org/abs/2104.12099v2 | MAE | 0.044 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | RD3D | https://arxiv.org/abs/2101.10241v1 | MAE | 0.045 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | HDFNet | https://arxiv.org/abs/2007.06227v3 | MAE | 0.048 |
10-shot image generation > Semantic Segmentation > Scene Segmentation > Thermal Image Segmentation | RGB-T-Glass-Segmentation | UTA | https://arxiv.org/abs/2109.03425v1 | MAE | 0.052 |
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