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 | UAVid | UNetFormer | https://arxiv.org/abs/2109.08937v4 | Mean IoU | 67.8 |
10-shot image generation > Semantic Segmentation | UAVid | DecoupleNet D2 | https://ieeexplore.ieee.org/document/10685518 | Mean IoU | 65.8 |
10-shot image generation > Semantic Segmentation | UAVid | BANet | https://arxiv.org/abs/2106.12413v2 | Mean IoU | 64.6 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | CMX (SegFormer-B4) | https://arxiv.org/abs/2203.04838v5 | mIoU | 62.1 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | CMX (SegFormer-B4) | https://arxiv.org/abs/2203.04838v5 | Pixel Accuracy | 82.6 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | CMX (SegFormer-B2) | https://arxiv.org/abs/2203.04838v5 | mIoU | 61.2 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | CMX (SegFormer-B2) | https://arxiv.org/abs/2203.04838v5 | Pixel Accuracy | 82.3 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | ShapeConv-101 | https://arxiv.org/abs/2108.10528v1 | mIoU | 60.6 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | ShapeConv-101 | https://arxiv.org/abs/2108.10528v1 | mAcc | 70.0 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | ShapeConv-101 | https://arxiv.org/abs/2108.10528v1 | Pixel Accuracy | 82.7 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | Linear Fusion (Segformer B2) | https://arxiv.org/abs/2304.10756v1 | mIoU | 57.16 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | MMAF-Net-152 | https://arxiv.org/abs/1912.11691v1 | mIoU | 52.9 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | MMAF-Net-152 | https://arxiv.org/abs/1912.11691v1 | mAcc | 62.3 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | MMAF-Net-152 | https://arxiv.org/abs/1912.11691v1 | Pixel Accuracy | 76.5 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | http://arxiv.org/abs/1803.06791v1 | mIoU | 39.5 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | http://arxiv.org/abs/1803.06791v1 | mAcc | 55.5 |
10-shot image generation > Semantic Segmentation | Stanford2D3D - RGBD | Depth-aware CNN | http://arxiv.org/abs/1803.06791v1 | Pixel Accuracy | 65.4 |
10-shot image generation > Semantic Segmentation | DADA-seg | MMUDA | https://arxiv.org/abs/2203.10395v1 | mIoU | 46.97 |
10-shot image generation > Semantic Segmentation | DADA-seg | Trans4Trans | https://arxiv.org/abs/2108.09174v1 | mIoU | 39.20 |
10-shot image generation > Semantic Segmentation | DADA-seg | EDCNet | https://arxiv.org/abs/2112.05006v1 | mIoU | 32.04 |
10-shot image generation > Semantic Segmentation | DADA-seg | SETR (PUP, Transformer-Large) | https://arxiv.org/abs/2012.15840v3 | mIoU | 31.8 |
10-shot image generation > Semantic Segmentation | DADA-seg | SETR (MLA, Transformer-Large) | https://arxiv.org/abs/2012.15840v3 | mIoU | 30.4 |
10-shot image generation > Semantic Segmentation | DADA-seg | ISSAFE | https://arxiv.org/abs/2008.08974v2 | mIoU | 29.97 |
10-shot image generation > Semantic Segmentation | DADA-seg | BDL | http://arxiv.org/abs/1904.10620v1 | mIoU | 29.66 |
10-shot image generation > Semantic Segmentation | DADA-seg | CLAN | http://arxiv.org/abs/1809.09478v3 | mIoU | 28.76 |
10-shot image generation > Semantic Segmentation | DADA-seg | HRNet (ACDC) | https://arxiv.org/abs/1908.07919v2 | mIoU | 27.5 |
10-shot image generation > Semantic Segmentation | DADA-seg | SegFormer (MiT-B3) | https://arxiv.org/abs/2105.15203v3 | mIoU | 27.0 |
10-shot image generation > Semantic Segmentation | DADA-seg | SIM | https://arxiv.org/abs/2003.08040v3 | mIoU | 26.85 |
10-shot image generation > Semantic Segmentation | DADA-seg | DeepLabV3+ (ACDC) | http://arxiv.org/abs/1802.02611v3 | mIoU | 26.8 |
10-shot image generation > Semantic Segmentation | DADA-seg | Fast-SCNN | http://arxiv.org/abs/1902.04502v1 | mIoU | 26.32 |
10-shot image generation > Semantic Segmentation | DADA-seg | Lawin Transformer | https://arxiv.org/abs/2201.01615v4 | mIoU | 25.16 |
10-shot image generation > Semantic Segmentation | DADA-seg | FDA | https://arxiv.org/abs/2004.05498v1 | mIoU | 24.45 |
10-shot image generation > Semantic Segmentation | DADA-seg | ResNet-101 | http://arxiv.org/abs/1512.03385v1 | mIoU | 23.60 |
10-shot image generation > Semantic Segmentation | DADA-seg | DANet | http://arxiv.org/abs/1809.02983v4 | mIoU | 22.24 |
10-shot image generation > Semantic Segmentation | DADA-seg | SegFormer (MiT-B2) | https://arxiv.org/abs/2105.15203v3 | mIoU | 21.2 |
10-shot image generation > Semantic Segmentation | DADA-seg | SwiftNet (ResNet-18) | http://arxiv.org/abs/1903.08469v2 | mIoU | 20.5 |
10-shot image generation > Semantic Segmentation | DADA-seg | PSPNet (ResNet-101) | http://arxiv.org/abs/1612.01105v2 | mIoU | 20.1 |
10-shot image generation > Semantic Segmentation | DADA-seg | ResNeSt (ResNeSt-101) | https://arxiv.org/abs/2004.08955v2 | mIoU | 19.99 |
10-shot image generation > Semantic Segmentation | DADA-seg | DNL (ResNet-101) | https://arxiv.org/abs/2006.06668v2 | mIoU | 19.7 |
10-shot image generation > Semantic Segmentation | DADA-seg | Semantic FPN (ResNet-101) | http://arxiv.org/abs/1901.02446v2 | mIoU | 19.59 |
10-shot image generation > Semantic Segmentation | DADA-seg | ResNet-50 | http://arxiv.org/abs/1512.03385v1 | mIoU | 18.96 |
10-shot image generation > Semantic Segmentation | DADA-seg | MobileNetV3 (MobileNetV3small) | https://arxiv.org/abs/1905.02244v5 | mIoU | 18.2 |
10-shot image generation > Semantic Segmentation | DADA-seg | SegFormer (MiT-B1) | https://arxiv.org/abs/2105.15203v3 | mIoU | 16.6 |
10-shot image generation > Semantic Segmentation | DADA-seg | MobileNetV2 | http://arxiv.org/abs/1801.04381v4 | mIoU | 16.05 |
10-shot image generation > Semantic Segmentation | DADA-seg | ERFNet | https://ieeexplore.ieee.org/abstract/document/8063438 | mIoU | 9.0 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (InternImage-H) | https://arxiv.org/abs/2505.19795v1 | Validation mIoU | 63.6 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (InternImage-H) | https://arxiv.org/abs/2505.19795v1 | Params (M) | 1610 |
10-shot image generation > Semantic Segmentation | ADE20K | ONE-PEACE | https://arxiv.org/abs/2305.11172v1 | Validation mIoU | 63.0 |
10-shot image generation > Semantic Segmentation | ADE20K | ONE-PEACE | https://arxiv.org/abs/2305.11172v1 | Params (M) | 1500 |
10-shot image generation > Semantic Segmentation | ADE20K | InternImage-H | https://arxiv.org/abs/2211.05778v4 | Validation mIoU | 62.9 |
10-shot image generation > Semantic Segmentation | ADE20K | InternImage-H | https://arxiv.org/abs/2211.05778v4 | Params (M) | 1310 |
10-shot image generation > Semantic Segmentation | ADE20K | InternImage-H | https://arxiv.org/abs/2211.05778v4 | GFLOPs | 4635 |
10-shot image generation > Semantic Segmentation | ADE20K | M3I Pre-training (InternImage-H) | https://arxiv.org/abs/2211.09807v2 | Validation mIoU | 62.9 |
10-shot image generation > Semantic Segmentation | ADE20K | M3I Pre-training (InternImage-H) | https://arxiv.org/abs/2211.09807v2 | Params (M) | 1310 |
10-shot image generation > Semantic Segmentation | ADE20K | BEiT-3 | https://arxiv.org/abs/2208.10442v2 | Validation mIoU | 62.8 |
10-shot image generation > Semantic Segmentation | ADE20K | BEiT-3 | https://arxiv.org/abs/2208.10442v2 | Params (M) | 1900 |
10-shot image generation > Semantic Segmentation | ADE20K | EVA | https://arxiv.org/abs/2211.07636v2 | Validation mIoU | 62.3 |
10-shot image generation > Semantic Segmentation | ADE20K | EVA | https://arxiv.org/abs/2211.07636v2 | Params (M) | 1074 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (OneFormer, InternImage-H) | https://arxiv.org/abs/2505.19795v1 | Validation mIoU | 61.6 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (OneFormer, InternImage-H) | https://arxiv.org/abs/2505.19795v1 | Params (M) | 1400 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (Mask2Former, BEiTv2 pretrain) | https://arxiv.org/abs/2205.08534v4 | Validation mIoU | 61.5 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (Mask2Former, BEiTv2 pretrain) | https://arxiv.org/abs/2205.08534v4 | Params (M) | 571 |
10-shot image generation > Semantic Segmentation | ADE20K | FD-SwinV2-G | https://arxiv.org/abs/2205.14141v3 | Validation mIoU | 61.4 |
10-shot image generation > Semantic Segmentation | ADE20K | FD-SwinV2-G | https://arxiv.org/abs/2205.14141v3 | Params (M) | 3000 |
10-shot image generation > Semantic Segmentation | ADE20K | RevCol-H (Mask2Former) | https://arxiv.org/abs/2212.11696v3 | Validation mIoU | 61.0 |
10-shot image generation > Semantic Segmentation | ADE20K | RevCol-H (Mask2Former) | https://arxiv.org/abs/2212.11696v3 | Params (M) | 2439 |
10-shot image generation > Semantic Segmentation | ADE20K | MasK DINO (SwinL, multi-scale) | https://arxiv.org/abs/2206.02777v3 | Validation mIoU | 60.8 |
10-shot image generation > Semantic Segmentation | ADE20K | MasK DINO (SwinL, multi-scale) | https://arxiv.org/abs/2206.02777v3 | Params (M) | 223 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (Mask2Former, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | Validation mIoU | 60.5 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (Mask2Former, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | Params (M) | 571 |
10-shot image generation > Semantic Segmentation | ADE20K | DINOv2 (ViT-g/14 frozen model, w/ ViT-Adapter + Mask2former) | https://arxiv.org/abs/2304.07193v2 | Validation mIoU | 60.2 |
10-shot image generation > Semantic Segmentation | ADE20K | DINOv2 (ViT-g/14 frozen model, w/ ViT-Adapter + Mask2former) | https://arxiv.org/abs/2304.07193v2 | Params (M) | 1080 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (OneFormer, DiNAT-L) | https://arxiv.org/abs/2505.19795v1 | Validation mIoU | 59.9 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-P (OneFormer, DiNAT-L) | https://arxiv.org/abs/2505.19795v1 | Params (M) | 309 |
10-shot image generation > Semantic Segmentation | ADE20K | SwinV2-G(UperNet) | https://arxiv.org/abs/2111.09883v2 | Validation mIoU | 59.9 |
10-shot image generation > Semantic Segmentation | ADE20K | PIIP-LH6B(UperNet) | https://arxiv.org/abs/2406.04330v2 | Validation mIoU | 59.9 |
10-shot image generation > Semantic Segmentation | ADE20K | SERNet-Former | https://arxiv.org/abs/2401.15741v7 | Validation mIoU | 59.35 |
10-shot image generation > Semantic Segmentation | ADE20K | FocalNet-L (Mask2Former) | https://arxiv.org/abs/2203.11926v3 | Validation mIoU | 58.5 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (UperNet, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | Validation mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K | ViT-Adapter-L (UperNet, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | Params (M) | 451 |
10-shot image generation > Semantic Segmentation | ADE20K | RSSeg-ViT-L (BEiT pretrain) | https://arxiv.org/abs/2212.13764v1 | Validation mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K | RSSeg-ViT-L (BEiT pretrain) | https://arxiv.org/abs/2212.13764v1 | Params (M) | 330 |
10-shot image generation > Semantic Segmentation | ADE20K | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | Validation mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | Params (M) | 316 |
10-shot image generation > Semantic Segmentation | ADE20K | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | GFLOPs (512 x 512) | 721 |
10-shot image generation > Semantic Segmentation | ADE20K | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | GFLOPs | 721 |
10-shot image generation > Semantic Segmentation | ADE20K | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | Mean IoU (class) | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K | SegViT-v2 (BEiT-v2-Large) | https://arxiv.org/abs/2306.06289v2 | Validation mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | ADE20K | SegViT-v2 (BEiT-v2-Large) | https://arxiv.org/abs/2306.06289v2 | GFLOPs (512 x 512) | 637.9 |
10-shot image generation > Semantic Segmentation | ADE20K | SeMask (SeMask Swin-L FaPN-Mask2Former) | https://arxiv.org/abs/2112.12782v3 | Validation mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | ADE20K | SeMask (SeMask Swin-L MSFaPN-Mask2Former) | https://arxiv.org/abs/2112.12782v3 | Validation mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | ADE20K | DiNAT-L (Mask2Former) | https://arxiv.org/abs/2209.15001v3 | Validation mIoU | 58.1 |
10-shot image generation > Semantic Segmentation | ADE20K | HorNet-L (Mask2Former) | https://arxiv.org/abs/2207.14284v3 | Validation mIoU | 57.9 |
10-shot image generation > Semantic Segmentation | ADE20K | Mask2Former (SwinL-FaPN) | https://arxiv.org/abs/2112.01527v3 | Validation mIoU | 57.7 |
10-shot image generation > Semantic Segmentation | ADE20K | FASeg (SwinL) | https://arxiv.org/abs/2204.01244v3 | Validation mIoU | 57.7 |
10-shot image generation > Semantic Segmentation | ADE20K | RR (BEiT-L) | https://arxiv.org/abs/2204.01969v1 | Validation mIoU | 57.7 |
10-shot image generation > Semantic Segmentation | ADE20K | MOAT-4 (IN-22K pretraining, single-scale) | https://arxiv.org/abs/2210.01820v2 | Validation mIoU | 57.6 |
10-shot image generation > Semantic Segmentation | ADE20K | MOAT-4 (IN-22K pretraining, single-scale) | https://arxiv.org/abs/2210.01820v2 | Params (M) | 496 |
10-shot image generation > Semantic Segmentation | ADE20K | Frozen Backbone, SwinV2-G-ext22K (Mask2Former) | https://arxiv.org/abs/2211.02043v1 | Validation mIoU | 57.6 |
10-shot image generation > Semantic Segmentation | ADE20K | SeMask (SeMask Swin-L Mask2Former) | https://arxiv.org/abs/2112.12782v3 | Validation mIoU | 57.5 |
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