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 | ImageNet-S | MAE (ViT-B/16, 224x224, SSL+FT) | https://arxiv.org/abs/2111.06377v2 | mIoU (test) | 60.2 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 59.4 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 59.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL+FT) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 58.9 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL+FT) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 57.8 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfmerge, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (val) | 51.3 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfmerge, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (test) | 51.1 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfmultiple, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (val) | 50.8 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfmultiple, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (test) | 50.5 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfsingle, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (val) | 50.7 |
10-shot image generation > Semantic Segmentation | ImageNet-S | RF-ConvNext-Tiny (rfsingle, P4, 224x224, SUP) | https://arxiv.org/abs/2206.06637v2 | mIoU (test) | 50.5 |
10-shot image generation > Semantic Segmentation | ImageNet-S | ConvNext-Tiny (P4, 224x224, SUP) | https://arxiv.org/abs/2201.03545v2 | mIoU (val) | 48.7 |
10-shot image generation > Semantic Segmentation | ImageNet-S | ConvNext-Tiny (P4, 224x224, SUP) | https://arxiv.org/abs/2201.03545v2 | mIoU (test) | 48.8 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-B/16, 100ep, 224x224, SSL) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 48.6 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-B/16, 100ep, 224x224, SSL) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 48.2 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL, mmseg) | https://arxiv.org/abs/2210.11016v1 | mIoU (val) | 46.1 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL, mmseg) | https://arxiv.org/abs/2210.11016v1 | mIoU (test) | 46.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL) | https://arxiv.org/abs/2210.11016v1 | mIoU (val) | 42.9 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL, mmseg) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 41.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL, mmseg) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 40.5 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 41.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-S/16, 100ep, 224x224, SSL) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 40.2 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL, mmseg) | https://arxiv.org/abs/2111.06377v2 | mIoU (val) | 40.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL, mmseg) | https://arxiv.org/abs/2111.06377v2 | mIoU (test) | 40.3 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL) | https://arxiv.org/abs/2111.06377v2 | mIoU (val) | 38.3 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL) | https://arxiv.org/abs/2111.06377v2 | mIoU (test) | 37.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | PASS (ResNet-50 D16, 224x224, LUSS) | https://arxiv.org/abs/2106.03149v3 | mIoU (val) | 21.6 |
10-shot image generation > Semantic Segmentation | ImageNet-S | PASS (ResNet-50 D16, 224x224, LUSS) | https://arxiv.org/abs/2106.03149v3 | mIoU (test) | 20.8 |
10-shot image generation > Semantic Segmentation | ImageNet-S | PASS (ResNet-50 D32, 224x224, LUSS) | https://arxiv.org/abs/2106.03149v3 | mIoU (val) | 21.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | PASS (ResNet-50 D32, 224x224, LUSS) | https://arxiv.org/abs/2106.03149v3 | mIoU (test) | 20.3 |
10-shot image generation > Semantic Segmentation | SBCoseg | Dice loss + IS-Triplet loss | https://arxiv.org/abs/2103.10670v1 | Jaccard | 0.951 |
10-shot image generation > Semantic Segmentation | dacl10k v1 testfinal | FPN EfficientNet-B4 | https://arxiv.org/abs/2309.00460v1 | mIoU | 42.4 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF80) | https://arxiv.org/abs/2303.01480v1 | mIoU (Real) | 83.11 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF80) | https://arxiv.org/abs/2303.01480v1 | mIoU (Syn) | 81.02 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF33) | https://arxiv.org/abs/2303.01480v1 | mIoU (Real) | 82.62 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF33) | https://arxiv.org/abs/2303.01480v1 | mIoU (Syn) | 80.98 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF8) | https://arxiv.org/abs/2303.01480v1 | mIoU (Real) | 83.22 |
10-shot image generation > Semantic Segmentation | UrbanLF | CMNeXt (RGB-LF8) | https://arxiv.org/abs/2303.01480v1 | mIoU (Syn) | 80.74 |
10-shot image generation > Semantic Segmentation | UrbanLF | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU (Real) | n.a. |
10-shot image generation > Semantic Segmentation | UrbanLF | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU (Syn) | 79.53 |
10-shot image generation > Semantic Segmentation | UrbanLF | ESANet | https://arxiv.org/abs/2011.06961v3 | mIoU (Real) | n.a. |
10-shot image generation > Semantic Segmentation | UrbanLF | ESANet | https://arxiv.org/abs/2011.06961v3 | mIoU (Syn) | 79.43 |
10-shot image generation > Semantic Segmentation | UrbanLF | OCR (HRNetV2-W48) | http://arxiv.org/abs/1505.04597v1 | mIoU (Real) | 78.60 |
10-shot image generation > Semantic Segmentation | UrbanLF | OCR (HRNetV2-W48) | http://arxiv.org/abs/1505.04597v1 | mIoU (Syn) | 79.36 |
10-shot image generation > Semantic Segmentation | UrbanLF | MTINet (HRNetV2-W48) | https://arxiv.org/abs/2001.06902v5 | mIoU (Real) | n.a. |
10-shot image generation > Semantic Segmentation | UrbanLF | MTINet (HRNetV2-W48) | https://arxiv.org/abs/2001.06902v5 | mIoU (Syn) | 79.10 |
10-shot image generation > Semantic Segmentation | UrbanLF | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU (Real) | 82.20 |
10-shot image generation > Semantic Segmentation | UrbanLF | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU (Syn) | 78.53 |
10-shot image generation > Semantic Segmentation | UrbanLF | SETR (ViT-Large) | https://arxiv.org/abs/2012.15840v3 | mIoU (Real) | 77.74 |
10-shot image generation > Semantic Segmentation | UrbanLF | SETR (ViT-Large) | https://arxiv.org/abs/2012.15840v3 | mIoU (Syn) | 77.69 |
10-shot image generation > Semantic Segmentation | UrbanLF | TMANet | https://arxiv.org/abs/2102.08643v2 | mIoU (Real) | 77.14 |
10-shot image generation > Semantic Segmentation | UrbanLF | TMANet | https://arxiv.org/abs/2102.08643v2 | mIoU (Syn) | 76.41 |
10-shot image generation > Semantic Segmentation | UrbanLF | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU (Real) | 76.34 |
10-shot image generation > Semantic Segmentation | UrbanLF | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU (Syn) | 75.78 |
10-shot image generation > Semantic Segmentation | UrbanLF | TDNet (ResNet-50) | https://arxiv.org/abs/2004.01800v2 | mIoU (Real) | 76.48 |
10-shot image generation > Semantic Segmentation | UrbanLF | TDNet (ResNet-50) | https://arxiv.org/abs/2004.01800v2 | mIoU (Syn) | 74.71 |
10-shot image generation > Semantic Segmentation | UrbanLF | DAVSS | https://arxiv.org/abs/2006.10380v2 | mIoU (Real) | 75.91 |
10-shot image generation > Semantic Segmentation | UrbanLF | DAVSS | https://arxiv.org/abs/2006.10380v2 | mIoU (Syn) | 74.27 |
10-shot image generation > Semantic Segmentation | UrbanLF | DeepLabV3+ (ResNet-101) | http://arxiv.org/abs/1802.02611v3 | mIoU (Real) | 76.27 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | SkyScapesNet-Dense | http://openaccess.thecvf.com/content_ICCV_2019/html/Azimi_SkyScapes__Fine-Grained_Semantic_Understanding_of_Aerial_Scenes_ICCV_2019_paper.html | Mean IoU | 40.13 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | DeepLabv3+ | http://arxiv.org/abs/1802.02611v3 | Mean IoU | 38.20 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | FCN8s (ResNet-50) | http://arxiv.org/abs/1411.4038v2 | Mean IoU | 33.06 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | BiSeNet (ResNet-50) | http://arxiv.org/abs/1808.00897v1 | Mean IoU | 30.82 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | DenseASPP (ResNet-101) | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.html | Mean IoU | 24.73 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | SegNet | http://arxiv.org/abs/1511.00561v3 | Mean IoU | 23.14 |
10-shot image generation > Semantic Segmentation | SkyScapes-Dense | U-Net | http://arxiv.org/abs/1505.04597v1 | Mean IoU | 14.15 |
10-shot image generation > Semantic Segmentation | ManipalUAVid | UVid-Net | https://arxiv.org/abs/2011.14284v2 | mIoU | 0.79 |
10-shot image generation > Semantic Segmentation | AI-TOD | Unet++(ResNet-50) | http://arxiv.org/abs/1807.10165v1 | Dice | 70.19 |
10-shot image generation > Semantic Segmentation | AI-TOD | DeepLabV3+(ResNet-50) | http://arxiv.org/abs/1802.02611v3 | Dice | 43.52 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | HRDA + PiPa | https://arxiv.org/abs/2211.07609v1 | Mean IoU | 68.2 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | MIC | https://arxiv.org/abs/2212.01322v2 | Mean IoU | 67.3 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | HRDA | https://arxiv.org/abs/2204.13132v2 | Mean IoU | 65.8 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | SePiCo | https://arxiv.org/abs/2204.08808v2 | Mean IoU | 64.3 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | DAFormer + ProCST | https://arxiv.org/abs/2204.11891v2 | Mean IoU | 61.6 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | DAFormer | https://arxiv.org/abs/2111.14887v2 | Mean IoU | 60.9 |
10-shot image generation > Semantic Segmentation | SYNTHIA-to-Cityscapes | TransDA-B | https://arxiv.org/abs/2203.07988v1 | Mean IoU | 59.3 |
10-shot image generation > Semantic Segmentation | COCO-Stuff full | SegFormer-B5 (Single Scale) | https://arxiv.org/abs/2105.15203v3 | Mean IoU (class) | 46.7 |
10-shot image generation > Semantic Segmentation | 38-Cloud | Cloud-Net+ | https://arxiv.org/abs/2001.08768v2 | Jaccard (Mean) | 88.90 |
10-shot image generation > Semantic Segmentation | 38-Cloud | Cloud-Net | http://arxiv.org/abs/1901.10077v1 | Jaccard (Mean) | 87.32 |
10-shot image generation > Semantic Segmentation | STARE | UNet | http://arxiv.org/abs/1505.04597v1 | AUC | 0.9158 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | EyeNet | https://arxiv.org/abs/2301.12972v3 | oAcc | 94.63 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | EyeNet | https://arxiv.org/abs/2301.12972v3 | mIoU | 81.13 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | PointNet++ | http://arxiv.org/abs/1706.02413v1 | oAcc | 91.2 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | PointNet++ | http://arxiv.org/abs/1706.02413v1 | mIoU | 56.5 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | RandLA-Net | https://arxiv.org/abs/1911.11236v3 | oAcc | 88.4 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | RandLA-Net | https://arxiv.org/abs/1911.11236v3 | mIoU | 74.3 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | CLOUDSPAM | https://www.mdpi.com/2072-4292/16/21/3984 | mIoU | 71.8 |
10-shot image generation > Semantic Segmentation | Toronto-3D L002 | DA-supervised | https://www.mdpi.com/2072-4292/16/21/3984 | mIoU | 69.3 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | EfficientNet-L2+NAS-FPN (single scale test, with self-training) | https://arxiv.org/abs/2006.06882v2 | mIoU | 90.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | TADP | https://arxiv.org/abs/2310.00031v3 | mIoU | 87.11% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | Eff-B7 NAS-FPN (Copy-Paste pre-training, single-scale)) | https://arxiv.org/abs/2012.07177v2 | mIoU | 86.6% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | ExFuse (ResNeXt-131) | http://arxiv.org/abs/1804.03821v1 | mIoU | 85.8% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | SpineNet-S143 (single-scale test) | https://arxiv.org/abs/2103.12270v1 | mIoU | 85.64% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | DeepLabv3-JFT | http://arxiv.org/abs/1706.05587v3 | mIoU | 82.7% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | Auto-DeepLab-L | http://arxiv.org/abs/1901.02985v2 | mIoU | 82.04% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | ResNet-GCN | http://arxiv.org/abs/1703.02719v1 | mIoU | 81.0% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | HyperSeg-L | https://arxiv.org/abs/2012.11582v2 | mIoU | 80.61% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | DFN (ResNet-101) | http://arxiv.org/abs/1804.09337v1 | mIoU | 80.60% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | WASPnet-CRF (ours) | https://arxiv.org/abs/1912.03183v1 | mIoU | 80.41% |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 val | Deeplab v3+ (Res2Net-101) | https://arxiv.org/abs/1904.01169v3 | mIoU | 79.3% |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.