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 | PASCAL Context | VPNeXt | https://arxiv.org/abs/2502.16654v1 | mIoU | 71.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | PlainSeg (EVA-02-L) | https://arxiv.org/abs/2310.12755v1 | mIoU | 71.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | InternImage-H | https://arxiv.org/abs/2211.05778v4 | mIoU | 70.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RSSeg-ViT-L (BEiT pretrain) | https://arxiv.org/abs/2212.13764v1 | mIoU | 68.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ViT-Adapter-L (Mask2Former, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | mIoU | 68.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ViT-Adapter-L (UperNet, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | mIoU | 67.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RSSeg-ViT-L | https://arxiv.org/abs/2212.13764v1 | mIoU | 67.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SegViT (ours) | https://arxiv.org/abs/2210.05844v2 | mIoU | 65.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CAA + CAR (ConvNeXt-Large + JPU) | https://arxiv.org/abs/2203.07160v2 | mIoU | 64.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SenFormer (Swin-L) | https://arxiv.org/abs/2111.13280v2 | mIoU | 64.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Sequential Ensemble (Segformer + HRNet) | https://arxiv.org/abs/2210.05387v1 | mIoU | 62.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CAA + Simple decoder (Efficientnet-B7) | https://arxiv.org/abs/2101.07434v5 | mIoU | 60.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DPT-Hybrid | https://arxiv.org/abs/2103.13413v1 | mIoU | 60.46 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CAA (Efficientnet-B7) | https://arxiv.org/abs/2101.07434v5 | mIoU | 60.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | HRNetV2 + OCR + RMI (PaddleClas pretrained) | https://arxiv.org/abs/1909.11065v6 | mIoU | 59.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Seg-L-Mask/16 | https://arxiv.org/abs/2105.05633v3 | mIoU | 59.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ResNeSt-269 | https://arxiv.org/abs/2004.08955v2 | mIoU | 58.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DEPICT-SA (ViT-L multi-scale) | https://arxiv.org/abs/2411.03033v3 | mIoU | 58.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ResNeSt-200 | https://arxiv.org/abs/2004.08955v2 | mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DEPICT-SA (ViT-L single-scale) | https://arxiv.org/abs/2411.03033v3 | mIoU | 57.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CondNet(ResNest-101) | https://arxiv.org/abs/2109.10322v1 | mIoU | 57 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SenFormer (ResNet-101) | https://arxiv.org/abs/2111.13280v2 | mIoU | 56.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ResNeSt-101 | https://arxiv.org/abs/2004.08955v2 | mIoU | 56.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | OCR (HRNetV2-W48) | https://arxiv.org/abs/1909.11065v6 | mIoU | 56.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | GPaCo (ResNet101) | https://arxiv.org/abs/2209.12400v2 | mIoU | 56.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CondNet(ResNet-101) | https://arxiv.org/abs/2109.10322v1 | mIoU | 56.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SETR-MLA (16, 80k, MS) | https://arxiv.org/abs/2012.15840v3 | mIoU | 55.83 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DCNAS | https://arxiv.org/abs/2003.11883v2 | mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DRAN(ResNet-101) | https://ieeexplore.ieee.org/document/9154612 | mIoU | 55.4% |
10-shot image generation > Semantic Segmentation | PASCAL Context | DNL | https://arxiv.org/abs/2006.06668v2 | mIoU | 55.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | HamNet (ResNet-101) | https://arxiv.org/abs/2109.04553v2 | mIoU | 55.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CAA (ResNet-101) | https://arxiv.org/abs/2101.07434v5 | mIoU | 55.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | mIoU | 54.8 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SIW(Segformer-B5) | https://arxiv.org/abs/2202.02002v2 | mIoU | 54.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CFNet (ResNet-101) | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Co-Occurrent_Features_in_Semantic_Segmentation_CVPR_2019_paper.html | mIoU | 54.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CFNet (ResNet-101) | https://arxiv.org/abs/1908.07919v2 | mIoU | 54.0 |
10-shot image generation > Semantic Segmentation | PASCAL Context | HRNetV2 HRNetV2-W48 | https://arxiv.org/abs/1908.07919v2 | mIoU | 54 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CPN(ResNet-101) | https://arxiv.org/abs/2004.01547v1 | mIoU | 53.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | LaU-regression-loss (ResNet-101) | https://arxiv.org/abs/1911.05250v2 | mIoU | 53.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DGCNet (MS, ResNet-101) | https://arxiv.org/abs/1909.06121v3 | mIoU | 53.7 |
10-shot image generation > Semantic Segmentation | PASCAL Context | BFP | https://arxiv.org/abs/1909.00179v1 | mIoU | 53.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SVCNet (ResNet-101) | https://arxiv.org/abs/1909.02651v1 | mIoU | 53.2 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Joint Pyramid Upsampling + EncNet | http://arxiv.org/abs/1903.11816v1 | mIoU | 53.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | EMANet | https://arxiv.org/abs/1907.13426v2 | mIoU | 53.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Asymmetric ALNN | https://arxiv.org/abs/1908.07678v5 | mIoU | 52.8 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CASSOD | https://arxiv.org/abs/2104.14126v1 | mIoU | 52.76 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DANet (ResNet-101) | http://arxiv.org/abs/1809.02983v4 | mIoU | 52.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ICM | http://openaccess.thecvf.com/content_CVPR_2019/html/Shi_Scene_Parsing_via_Integrated_Classification_Model_and_Variance-Based_Regularization_CVPR_2019_paper.html | mIoU | 52.60 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DUpsampling | http://arxiv.org/abs/1903.02120v3 | mIoU | 52.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | EncNet (ResNet-101) | http://arxiv.org/abs/1803.08904v1 | mIoU | 51.7 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CFNet (ResNet-50) | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Co-Occurrent_Features_in_Semantic_Segmentation_CVPR_2019_paper.html | mIoU | 51.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ResNet-38 | http://arxiv.org/abs/1611.10080v1 | mIoU | 48.1 |
10-shot image generation > Semantic Segmentation | PASCAL Context | PSPNet (ResNet-101) | http://arxiv.org/abs/1612.01105v2 | mIoU | 47.8 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RefineNet | http://arxiv.org/abs/1611.06612v3 | mIoU | 47.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | DeepLabV2 | http://arxiv.org/abs/1606.00915v2 | mIoU | 45.7 |
10-shot image generation > Semantic Segmentation | PASCAL Context | VeryDeep | http://arxiv.org/abs/1605.06885v1 | mIoU | 44.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Piecewise | http://arxiv.org/abs/1504.01013v4 | mIoU | 43.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | Dilated-FCN2s | http://arxiv.org/abs/1707.08254v3 | mIoU | 42.6 |
10-shot image generation > Semantic Segmentation | PASCAL Context | HO CRF | http://arxiv.org/abs/1511.08119v4 | mIoU | 41.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | BoxSup | http://arxiv.org/abs/1503.01640v2 | mIoU | 40.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | ParseNet | http://arxiv.org/abs/1506.04579v2 | mIoU | 40.4 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CRF-RNN | http://arxiv.org/abs/1502.03240v3 | mIoU | 39.3 |
10-shot image generation > Semantic Segmentation | PASCAL Context | FCN-8s | http://arxiv.org/abs/1411.4038v2 | mIoU | 37.8 |
10-shot image generation > Semantic Segmentation | PASCAL Context | CFM | http://arxiv.org/abs/1412.1283v4 | mIoU | 34.4 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RBE2E | http://arxiv.org/abs/1607.07671v1 | mIoU | 32.5 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RBE2E | http://arxiv.org/abs/1607.07671v1 | Mean Accuracy | 49.9 |
10-shot image generation > Semantic Segmentation | PASCAL Context | RBE2E | http://arxiv.org/abs/1607.07671v1 | Pixel Accuracy | 62.4 |
10-shot image generation > Semantic Segmentation | PASCAL Context | SegCLIP | https://arxiv.org/abs/2211.14813v2 | mIoU | 24.7 |
10-shot image generation > Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | Ensemble-04 MiT-0 MiT-1 RNX-1 RNX-2 | https://openaccess.thecvf.com/content/WACV2024W/CV4EO/html/Straka_Modernized_Training_of_U-Net_for_Aerial_Semantic_Segmentation_WACVW_2024_paper.html | mIoU | 64.1 |
10-shot image generation > Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | LF-DLM | https://arxiv.org/abs/2410.00469v1 | mIoU | 63.1 |
10-shot image generation > Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | U-T&T | https://arxiv.org/abs/2310.13336v1 | mIoU | 58.6 |
10-shot image generation > Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | U-Net | https://arxiv.org/abs/2310.13336v1 | mIoU | 54.7 |
10-shot image generation > Semantic Segmentation | FLAIR (French Land cover from Aerospace ImageRy) | U-Net baseline | https://arxiv.org/abs/2211.12979v5 | mIoU | 0.557 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | FoodSAM | https://arxiv.org/abs/2308.05938v1 | mIoU | 46.4 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | SeTR-MLA (ViT-16/B) | https://arxiv.org/abs/2012.15840v3 | mIoU | 45.1 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | SeTR-Naive (ReLeM-ViT-16/B) | https://arxiv.org/abs/2105.05409v1 | mIoU | 43.9 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | Swin-Transformer (Swin-Small) | https://arxiv.org/abs/2103.14030v2 | mIoU | 41.6 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | SeTR-Naive (ViT-16/B) | https://arxiv.org/abs/2012.15840v3 | mIoU | 41.3 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | CCNet (ReLeM-ResNet-50) | https://arxiv.org/abs/2105.05409v1 | mIoU | 36.8 |
10-shot image generation > Semantic Segmentation | FoodSeg103 | CCNet (ResNet-50) | https://arxiv.org/abs/1811.11721v2 | mIoU | 35.5 |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | ScribFormer | https://arxiv.org/abs/2402.02029v1 | Dice (Average) | 88.8% |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | ScribbleVC | https://arxiv.org/abs/2307.16226v1 | Dice (Average) | 88.4% |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | CycleMix | https://arxiv.org/abs/2203.01475v2 | Dice (Average) | 84.8% |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | CutMix | https://arxiv.org/abs/1905.04899v2 | Dice (Average) | 70.5% |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | TFCNs | https://arxiv.org/abs/2207.03450v1 | Dice (Average) | 64.5% |
10-shot image generation > Semantic Segmentation | ACDC Scribbles | Puzzle Mix | https://arxiv.org/abs/2009.06962v2 | Dice (Average) | 62.4% |
10-shot image generation > Semantic Segmentation | Hypersim | EMSANet (2x ResNet-34 NBt1D) | https://arxiv.org/abs/2309.13635v2 | mIoU | 49.74 |
10-shot image generation > Semantic Segmentation | Hypersim | EMSANet (2x ResNet-34 NBt1D) | https://arxiv.org/abs/2309.13635v2 | mIoU (test) | 46.66 |
10-shot image generation > Semantic Segmentation | Hypersim | MultiMAE (ViT-B) | https://arxiv.org/abs/2204.01678v1 | mIoU | 37.0 |
10-shot image generation > Semantic Segmentation | Hypersim | MAE (ViT-B) | https://arxiv.org/abs/2204.01678v1 | mIoU | 36.5 |
10-shot image generation > Semantic Segmentation | Hypersim | DINO (ViT-B) | https://arxiv.org/abs/2204.01678v1 | mIoU | 32.5 |
10-shot image generation > Semantic Segmentation | Hypersim | MoCo-v3 (ViT-B) | https://arxiv.org/abs/2204.01678v1 | mIoU | 31.7 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2210.11016v1 | mIoU (val) | 63.2 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2210.11016v1 | mIoU (test) | 62.5 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-B/16, 100ep, 224x224, SSL+FT) | https://arxiv.org/abs/2206.05184v3 | mIoU (val) | 63.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | SERE (ViT-B/16, 100ep, 224x224, SSL+FT) | https://arxiv.org/abs/2206.05184v3 | mIoU (test) | 63.3 |
10-shot image generation > Semantic Segmentation | ImageNet-S | TEC (ViT-B/16, 224x224, SSL+FT) | https://arxiv.org/abs/2210.11016v1 | mIoU (val) | 62.0 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2111.06377v2 | mIoU (val) | 61.6 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL+FT, mmseg) | https://arxiv.org/abs/2111.06377v2 | mIoU (test) | 61.2 |
10-shot image generation > Semantic Segmentation | ImageNet-S | MAE (ViT-B/16, 224x224, SSL+FT) | https://arxiv.org/abs/2111.06377v2 | mIoU (val) | 61.0 |
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