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 | S3DIS | PointNet | http://arxiv.org/abs/1612.00593v2 | Number of params | null |
10-shot image generation > Semantic Segmentation | S3DIS | BIM-Net | https://ieeexplore.ieee.org/document/10222064 | mIoU | 59.5 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DeepLabv3+ (Xception-65-JFT) | http://arxiv.org/abs/1802.02611v3 | Mean IoU | 89.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DeepLabv3+ (Xception-JFT) | http://arxiv.org/abs/1802.02611v3 | Mean IoU | 89.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DeepLabv3-JFT | http://arxiv.org/abs/1706.05587v3 | Mean IoU | 86.9% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | CASIA_IVA_SDN | http://arxiv.org/abs/1708.04943v1 | Mean IoU | 86.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Smooth Network with Channel Attention Block | http://arxiv.org/abs/1804.09337v1 | Mean IoU | 86.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | SANet (pretraining on COCO dataset) | https://arxiv.org/abs/1909.03402v4 | Mean IoU | 86.1% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EncNet | http://arxiv.org/abs/1803.08904v1 | Mean IoU | 85.9% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | HamNet w/o COCO (ResNet-101) | https://arxiv.org/abs/2109.04553v2 | Mean IoU | 85.9% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Auto-DeepLab-L | http://arxiv.org/abs/1901.02985v2 | Mean IoU | 85.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | PSPNet | http://arxiv.org/abs/1612.01105v2 | Mean IoU | 85.4% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | ResNet-38 MS COCO | http://arxiv.org/abs/1611.10080v1 | Mean IoU | 84.9% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | OCR (HRNetV2-W48) | https://arxiv.org/abs/1909.11065v6 | Mean IoU | 84.5% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | Mean IoU | 84.3% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Multipath-RefineNet | http://arxiv.org/abs/1611.06612v3 | Mean IoU | 84.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | ShelfNet | https://arxiv.org/abs/1811.11254v6 | Mean IoU | 84.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EANet (ResNet-101) | https://arxiv.org/abs/2105.02358v2 | Mean IoU | 84% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Large Kernel Matters | http://arxiv.org/abs/1703.02719v1 | Mean IoU | 83.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | TripleNet | http://arxiv.org/abs/1809.09299v1 | Mean IoU | 83.3% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | SANet | https://arxiv.org/abs/1909.03402v4 | Mean IoU | 83.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | TuSimple | http://arxiv.org/abs/1702.08502v3 | Mean IoU | 83.1% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EncNet (ResNet-101) | http://arxiv.org/abs/1803.08904v1 | Mean IoU | 82.9% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DFN (ResNet-101) | http://arxiv.org/abs/1804.09337v1 | Mean IoU | 82.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Deep Layer Cascade (LC) | http://arxiv.org/abs/1704.01344v1 | Mean IoU | 82.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Light-Weight-RefineNet-152 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 82.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DANet (ResNet-101) | http://arxiv.org/abs/1809.02983v4 | Mean IoU | 82.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | PSPNet (ResNet-101) | http://arxiv.org/abs/1612.01105v2 | Mean IoU | 82.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Light-Weight-RefineNet-101 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 82.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Light-Weight-RefineNet-50 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 81.1% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | CentraleSupelec Deep G-CRF | http://arxiv.org/abs/1603.08358v4 | Mean IoU | 80.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EdgeNeXt | https://arxiv.org/abs/2206.10589v3 | Mean IoU | 80.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EdgeNeXt | https://arxiv.org/abs/2206.10589v3 | FLOPS | 8.7G |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | EdgeNeXt | https://arxiv.org/abs/2206.10589v3 | Params | 6.5M |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DeepLab-CRF (ResNet-101) | http://arxiv.org/abs/1606.00915v2 | Mean IoU | 79.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | WASPnet-CRF (ours) | https://arxiv.org/abs/1912.03183v1 | Mean IoU | 79.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Light-Weight-RefineNet-MobileNet-v2 | http://arxiv.org/abs/1810.03272v1 | Mean IoU | 79.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Deeplab-v2 with Lovasz-Softmax loss | http://arxiv.org/abs/1705.08790v2 | Mean IoU | 79.00% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Deeplab-v2 + Lovász-Softmax | http://arxiv.org/abs/1705.08790v2 | Mean IoU | 79.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | CRF-RNN | http://arxiv.org/abs/1502.03240v3 | Mean IoU | 74.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | SID | http://arxiv.org/abs/1603.07485v2 | Mean IoU | 72.8% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DeepLab-MSc-CRF-LargeFOV (VGG-16) | http://arxiv.org/abs/1412.7062v4 | Mean IoU | 71.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | ParseNet | http://arxiv.org/abs/1506.04579v2 | Mean IoU | 69.8% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Dilated FCN-2s VGG19 | http://arxiv.org/abs/1707.08254v3 | Mean IoU | 69% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | ESPNetv2 | http://arxiv.org/abs/1811.11431v3 | Mean IoU | 68.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | Dilated Convolutions | http://arxiv.org/abs/1511.07122v3 | Mean IoU | 67.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | DiCENet | https://arxiv.org/abs/1906.03516v3 | Mean IoU | 67.31% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | RRM | https://arxiv.org/abs/1911.08039v1 | Mean IoU | 66.5 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | SSDD | https://arxiv.org/abs/1911.01370v2 | Mean IoU | 65.5 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | BoxSup | http://arxiv.org/abs/1503.01640v2 | Mean IoU | 64.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | PSA w/ EADER DeepLab (Xception-65) | https://arxiv.org/abs/2011.04626v1 | Mean IoU | 63.8% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | ESPNet | http://arxiv.org/abs/1803.06815v3 | Mean IoU | 63.01% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | FCN (VGG-16) | http://arxiv.org/abs/1411.4038v2 | Mean IoU | 62.2% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | G2 | http://arxiv.org/abs/1701.08261v2 | Mean IoU | 56.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 test | CK | http://arxiv.org/abs/1407.1808v1 | Mean IoU | 51.6% |
10-shot image generation > Semantic Segmentation | GAMUS | TIMF | https://arxiv.org/abs/2305.14914v1 | mIoU | 76.38 |
10-shot image generation > Semantic Segmentation | GAMUS | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 75.23 |
10-shot image generation > Semantic Segmentation | GAMUS | VCD | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Variational_Context-Deformable_ConvNets_for_Indoor_Scene_Parsing_CVPR_2020_paper.html | mIoU | 59.70 |
10-shot image generation > Semantic Segmentation | GAMUS | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIoU | 58.26 |
10-shot image generation > Semantic Segmentation | GAMUS | ShapeConv | https://arxiv.org/abs/2108.10528v1 | mIoU | 55.86 |
10-shot image generation > Semantic Segmentation | GAMUS | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIoU | 52.73 |
10-shot image generation > Semantic Segmentation | ATLANTIS | Erfani et al. | https://arxiv.org/abs/2111.11567v1 | A-acc | 68.63 |
10-shot image generation > Semantic Segmentation | ATLANTIS | Erfani et al. | https://arxiv.org/abs/2111.11567v1 | A-mIoU | 50.34 |
10-shot image generation > Semantic Segmentation | ATLANTIS | Erfani et al. | https://arxiv.org/abs/2111.11567v1 | Accuracy | 75.18 |
10-shot image generation > Semantic Segmentation | ATLANTIS | Erfani et al. | https://arxiv.org/abs/2111.11567v1 | mIoU | 42.22 |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | VPNeXt | https://arxiv.org/abs/2502.16654v1 | mIoU | 53.7 |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | ViT-P (InternImage-H) | https://arxiv.org/abs/2505.19795v1 | mIoU | 53.5 |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | EVA | https://arxiv.org/abs/2211.07636v2 | mIoU | 53.4 |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | RSSeg-ViT-L (BEiT pretrain) | https://arxiv.org/abs/2212.13764v1 | mIoU | 52.6% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | RSSeg-ViT-L | https://arxiv.org/abs/2212.13764v1 | mIoU | 52.0% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | SegViT (ours) | https://arxiv.org/abs/2210.05844v2 | mIoU | 50.3% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | SenFormer (Swin-L) | https://arxiv.org/abs/2111.13280v2 | mIoU | 50.1% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | CAA (Efficientnet-B7) | https://arxiv.org/abs/2101.07434v5 | mIoU | 45.4% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | HRNetV2 + OCR + RMI (PaddleClas pretrained) | https://arxiv.org/abs/1909.11065v6 | mIoU | 45.2% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | DRAN(ResNet-101) | https://ieeexplore.ieee.org/document/9154612 | mIoU | 41.2% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | CAA (ResNet-101) | https://arxiv.org/abs/2101.07434v5 | mIoU | 41.2% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | OCR (HRNetV2-W48) | https://arxiv.org/abs/1909.11065v6 | mIoU | 40.5% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | EMANet | https://arxiv.org/abs/1907.13426v2 | mIoU | 39.9% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | DANet (ResNet-101) | http://arxiv.org/abs/1809.02983v4 | mIoU | 39.7% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | SVCNet (ResNet-101) | https://arxiv.org/abs/1909.02651v1 | mIoU | 39.6% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | mIoU | 39.5% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | Asymmetric ALNN | https://arxiv.org/abs/1908.07678v5 | mIoU | 37.2% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | CCL (ResNet-101) | http://openaccess.thecvf.com/content_cvpr_2018/html/Ding_Context_Contrasted_Feature_CVPR_2018_paper.html | mIoU | 35.7% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | RefineNet (ResNet-101) | http://arxiv.org/abs/1611.06612v3 | mIoU | 33.6% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | DAG-RNN (VGG-16) | http://arxiv.org/abs/1509.00552v2 | mIoU | 31.2% |
10-shot image generation > Semantic Segmentation | COCO-Stuff test | FCN (VGG-16) | http://arxiv.org/abs/1411.4038v2 | mIoU | 22.7% |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | AerialFormer-B | https://arxiv.org/abs/2306.06842v2 | Overall Accuracy | 93.9 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | AerialFormer-B | https://arxiv.org/abs/2306.06842v2 | Mean F1 | 94.1 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | AerialFormer-B | https://arxiv.org/abs/2306.06842v2 | Mean IoU | 89.1 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | ViT-G12X4 | https://arxiv.org/abs/2304.05215v4 | Overall Accuracy | 92.58 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | ViT-G12X4 | https://arxiv.org/abs/2304.05215v4 | Mean F1 | 92.12 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | FT-UNetFormer | https://arxiv.org/abs/2109.08937v4 | Overall Accuracy | 92.0 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | FT-UNetFormer | https://arxiv.org/abs/2109.08937v4 | Mean F1 | 93.3 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | FT-UNetFormer | https://arxiv.org/abs/2109.08937v4 | Mean IoU | 87.5 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | DC-Swin | https://arxiv.org/abs/2104.12137v6 | Overall Accuracy | 92.0 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | DC-Swin | https://arxiv.org/abs/2104.12137v6 | Mean F1 | 93.25 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | DC-Swin | https://arxiv.org/abs/2104.12137v6 | Mean IoU | 87.56 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | LSKNet-S | https://arxiv.org/abs/2403.11735v5 | Overall Accuracy | 92.0 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | LSKNet-S | https://arxiv.org/abs/2403.11735v5 | Mean F1 | 93.1 |
10-shot image generation > Semantic Segmentation | ISPRS Potsdam | LSKNet-S | https://arxiv.org/abs/2403.11735v5 | Mean IoU | 87.2 |
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