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 | SUN-RGBD | FSFNet | https://arxiv.org/abs/1912.11691v1 | Mean IoU | 47.0% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | PSD-ResNet50 | http://openaccess.thecvf.com/content_iccv_2017/html/Qi_3D_Graph_Neural_ICCV_2017_paper.html | Mean IoU | 45.9% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (S) | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 45.73 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DPLNet | http://arxiv.org/abs/1705.07238v2 | Mean IoU | 45.1% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-L | https://arxiv.org/abs/2107.13800v2 | Mean IoU | 44.3% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | TokenFusion (S) | http://arxiv.org/abs/1803.06791v1 | Mean IoU | 42.0% |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DPLNet | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 38.4 |
10-shot image generation > Semantic Segmentation | SUN-RGBD | DFormer-L | https://arxiv.org/abs/2304.10756v1 | Mean IoU (test) | 48.17 |
10-shot image generation > Semantic Segmentation | DSEC | BRENet | https://arxiv.org/abs/2505.01548v1 | mIoU | 74.94 |
10-shot image generation > Semantic Segmentation | DSEC | CMNeXt | https://arxiv.org/abs/2303.01480v1 | mIoU | 72.54 |
10-shot image generation > Semantic Segmentation | DSEC | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 72.42 |
10-shot image generation > Semantic Segmentation | DSEC | SegFormer-B2 | https://arxiv.org/abs/2105.15203v3 | mIoU | 71.99 |
10-shot image generation > Semantic Segmentation | DSEC | SegNeXt-B | https://arxiv.org/abs/2209.08575v1 | mIoU | 71.55 |
10-shot image generation > Semantic Segmentation | DSEC | EDCNet-S2D | https://arxiv.org/abs/2112.05006v1 | mIoU | 56.75 |
10-shot image generation > Semantic Segmentation | DSEC | HALSIE | https://arxiv.org/abs/2211.10754v4 | mIoU | 52.43 |
10-shot image generation > Semantic Segmentation | DSEC | EV-SegNet | http://arxiv.org/abs/1811.12039v1 | mIoU | 51.76 |
10-shot image generation > Semantic Segmentation | DSEC | ESS | https://arxiv.org/abs/2203.10016v2 | mIoU | 51.57 |
10-shot image generation > Semantic Segmentation | BDD | FasterSeg | https://arxiv.org/abs/1912.10917v2 | mIoU | 55.1 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | RoadFormer+ (B) | https://arxiv.org/abs/2407.21631v2 | IoU | 94.11 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | RoadFormer (L) | https://arxiv.org/abs/2309.10356v4 | IoU | 93.51 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | CMX | https://arxiv.org/abs/2203.04838v5 | IoU | 93.31 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | RoadFormer (B) | https://arxiv.org/abs/2309.10356v4 | IoU | 93.06 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | SNE-RoadSeg | https://arxiv.org/abs/2008.11351v1 | IoU | 92.10 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | CAINet | https://arxiv.org/abs/2401.01624v1 | IoU | 91.77 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | DFormer | https://arxiv.org/abs/2309.09668v2 | IoU | 90.88 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | IoU | 90.50 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | IoU | 87.70 |
10-shot image generation > Semantic Segmentation | SYN-UDTIRI | OFF-Net | https://arxiv.org/abs/2206.09907v2 | IoU | 83.80 |
10-shot image generation > Semantic Segmentation | OpenEDS | RITnet | https://arxiv.org/abs/1910.00694v1 | mIOU | 95.3 |
10-shot image generation > Semantic Segmentation | uyfds | Poi | https://arxiv.org/abs/1911.05030v1 | 0-shot MRR | P |
10-shot image generation > Semantic Segmentation | Synthetic Bathing Perception | CMX-SRA | https://arxiv.org/abs/2203.04838v5 | mIoU | 94.20 |
10-shot image generation > Semantic Segmentation | Synthetic Bathing Perception | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 88.23 |
10-shot image generation > Semantic Segmentation | Synthetic Bathing Perception | RTFNet | https://ieeexplore.ieee.org/abstract/document/8666745 | mIoU | 87.49 |
10-shot image generation > Semantic Segmentation | Synthetic Bathing Perception | MFNet | https://ieeexplore.ieee.org/abstract/document/8206396 | mIoU | 87.25 |
10-shot image generation > Semantic Segmentation | Synthetic Bathing Perception | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU | 86.86 |
10-shot image generation > Semantic Segmentation | Endoscapes | MoCo V2 Surg SSL - DeepLabv3+ head | https://arxiv.org/abs/2207.00449v3 | Mean F1 | 73.2 |
10-shot image generation > Semantic Segmentation | Endoscapes | TCNN | https://arxiv.org/abs/2112.13815v1 | Mean F1 | 73.1 |
10-shot image generation > Semantic Segmentation | Mila Simulated Floods | FloodTransformer (Ours) | https://arxiv.org/abs/2210.04218v1 | mIoU | 0.93 |
10-shot image generation > Semantic Segmentation | Graz-02 | VOLO-D5 | https://arxiv.org/abs/2106.13112v2 | Pixel Accuracy | 85 |
10-shot image generation > Semantic Segmentation | Graz-02 | FloodTransformer (Ours) | https://arxiv.org/abs/2210.04218v1 | Pixel Accuracy | 0.96 |
10-shot image generation > Semantic Segmentation | Cleargrasp (Novel) | Cleargrasp | https://arxiv.org/abs/1910.02550v2 | Mean IoU | 58 |
10-shot image generation > Semantic Segmentation | Cleargrasp (Novel) | SuperCaustics-R | https://arxiv.org/abs/2107.11008v2 | Mean IoU | 53.16 |
10-shot image generation > Semantic Segmentation | Cleargrasp (Novel) | SuperCaustics-R | https://arxiv.org/abs/2107.11008v2 | Accuracy | 95.6 |
10-shot image generation > Semantic Segmentation | UTFPR-SBD3 | EPYNET | https://ieeexplore.ieee.org/document/9222020 | IoU | 51,02 |
10-shot image generation > Semantic Segmentation | UTFPR-SBD3 | EPYNET | https://ieeexplore.ieee.org/document/9222020 | 1:1 Accuracy | 92,06 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | MMSFormer (RGB-A-D) | https://arxiv.org/abs/2309.04001v4 | mIoU | 52.03 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | MMSFormer (RGB-A) | https://arxiv.org/abs/2309.04001v4 | mIoU | 51.30 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | ShareCMP (B2 RGB-A-D) | https://arxiv.org/abs/2312.03430v2 | mIoU | 50.99 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | ShareCMP (B2 RGB-D) | https://arxiv.org/abs/2312.03430v2 | mIoU | 50.55 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | MMSFormer (RGB) | https://arxiv.org/abs/2309.04001v4 | mIoU | 50.44 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | ShareCMP(B2 RGB-A) | https://arxiv.org/abs/2312.03430v2 | mIoU | 50.34 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | CMNeXt (B2 RGB-A-D) | https://arxiv.org/abs/2303.01480v1 | mIoU | 49.48 |
10-shot image generation > Semantic Segmentation | MCubeS (P) | CMNeXt (B2 RGB-A) | https://arxiv.org/abs/2303.01480v1 | mIoU | 48.42 |
10-shot image generation > Semantic Segmentation | FP4S | FP4S | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4727643 | Dice (Average) | 0.34 |
10-shot image generation > Semantic Segmentation | DIVA-HisDB | U-Net | https://arxiv.org/abs/2201.08295v3 | Mean IoU (class) | 97.26 |
10-shot image generation > Semantic Segmentation | DIVA-HisDB | IJNS '23 (few-shot) | https://www.worldscientific.com/doi/10.1142/S0129065723500521?srsltid=AfmBOooj60Tfvwgncg7FpFMhlbZbZy5GqpffiGLkilRa6fOi4tKg-KQ- | Mean IoU (class) | 97.23 |
10-shot image generation > Semantic Segmentation | DIVA-HisDB | WACV '23 (few-shot) | https://arxiv.org/abs/2210.15570v1 | Mean IoU (class) | 96.30 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | MAE+MTP(ViT-L) | https://arxiv.org/abs/2403.13430v2 | Mean IoU | 79.69 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | MAE+MTP(ViT-B+RVSA) | https://arxiv.org/abs/2403.13430v2 | Mean IoU | 79.63 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | MAE+MTP(ViT-L+RVSA) | https://arxiv.org/abs/2403.13430v2 | Mean IoU | 79.54 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | SelectiveMAE+ViT-B | https://arxiv.org/abs/2406.11933v4 | Mean IoU | 79.50 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | IMP+MTP(InternImage-XL) | https://arxiv.org/abs/2403.13430v2 | Mean IoU | 79.16 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | PSANet w/ ResNet50 - FMoW self-supervised pre-training w/ MoCo-V2 + Temporal Positives | https://arxiv.org/abs/2011.09980v7 | Mean IoU | 78.48 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | PSANet w/ ResNet50 backbone - FMoW self-supervised pre-training w/ MoCo-V2 | https://arxiv.org/abs/2011.09980v7 | Mean IoU | 78.05 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | PSANet w/ ResNet50 backbone - FMoW pretrained | https://arxiv.org/abs/2011.09980v7 | Mean IoU | 75.57 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | PSANet w/ ResNet50 backbone - ImageNet pretrained | https://arxiv.org/abs/2011.09980v7 | Mean IoU | 75.23 |
10-shot image generation > Semantic Segmentation | SpaceNet 1 | PSANet w/ ResNet50 backbone | https://arxiv.org/abs/2011.09980v7 | Mean IoU | 74.93 |
10-shot image generation > Semantic Segmentation | DroneDeploy | DLv3+ (Xception65) | https://arxiv.org/abs/2012.02024v1 | Mean IoU (val) | 69.9 |
10-shot image generation > Semantic Segmentation | DroneDeploy | DLv3+ (Xception65) | https://arxiv.org/abs/2012.02024v1 | Mean IoU (test) | 52.5 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | SMMCL (SegNeXt-B) | https://arxiv.org/abs/2308.12320v2 | mIoU | 68.76 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | SMMCL (SegFormer-B2) | https://arxiv.org/abs/2308.12320v2 | mIoU | 67.77 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | CMX (SegFormer-B2) | https://arxiv.org/abs/2203.04838v5 | mIoU | 66.52 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | TokenFusion (SegFormer-B2) | https://arxiv.org/abs/2204.08721v2 | mIoU | 64.75 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | SMMCL (ResNet-101) | https://arxiv.org/abs/2308.12320v2 | mIoU | 64.40 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | ShapeConv (ResNeXt-101) | https://arxiv.org/abs/2108.10528v1 | mIoU | 63.26 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | CEN (ResNet-101) | https://arxiv.org/abs/2112.02252v2 | mIoU | 62.15 |
10-shot image generation > Semantic Segmentation | LLRGBD-synthetic | SA-Gate (ResNet-101) | https://arxiv.org/abs/2007.09183v1 | mIoU | 61.79 |
10-shot image generation > Semantic Segmentation | ScanNetV2 | CMX | https://arxiv.org/abs/2203.04838v5 | Mean IoU | 61.3% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version) | https://arxiv.org/abs/2309.13635v2 | Mean IoU | 60.0% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version) | https://arxiv.org/abs/2309.13635v2 | Mean IoU (val) | 70.99% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version) | https://arxiv.org/abs/2309.13635v2 | Mean IoU (test) | 60.0% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | RFBNet | https://arxiv.org/abs/1907.00135v2 | Mean IoU | 59.2% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | SSMA | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 57.7 |
10-shot image generation > Semantic Segmentation | ScanNetV2 | EMSAFormer | https://arxiv.org/abs/2306.05242v1 | Mean IoU | 56.4% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | AdapNet++ | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 50.3 |
10-shot image generation > Semantic Segmentation | ScanNetV2 | 3DMV (2d proj) | http://arxiv.org/abs/1803.10409v1 | Mean IoU | 49.8% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | MSeg1080_RVC | https://arxiv.org/abs/2112.13762v1 | Mean IoU | 48.5% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | PSPNet | http://arxiv.org/abs/1612.01105v2 | Mean IoU | 47.5% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | ENet | http://arxiv.org/abs/1606.02147v1 | Mean IoU | 37.6% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | ScanNet (2d proj) | http://arxiv.org/abs/1702.04405v2 | Mean IoU | 33.0% |
10-shot image generation > Semantic Segmentation | ScanNetV2 | Floors are Flat | https://arxiv.org/abs/1906.06792v1 | Pixel Accuracy | 65.6 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | OmniVec2 | http://openaccess.thecvf.com//content/CVPR2024/html/Srivastava_OmniVec2_-_A_Novel_Transformer_based_Network_for_Large_Scale_CVPR_2024_paper.html | Mean IoU | 63.6 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DiffusionMMS (DAT++-S) | https://arxiv.org/abs/2409.15117v2 | Mean IoU | 61.5 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DepthMatch (DINOv2-S) | https://arxiv.org/abs/2505.20041v1 | Mean IoU | 61.4 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | GeminiFusion (Swin-Large) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 60.9 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | OmniVec | https://arxiv.org/abs/2311.05709v1 | Mean IoU | 60.8 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | GeminiFusion (Swin-Large) | https://arxiv.org/abs/2406.01210v2 | Mean IoU | 60.2 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | DPLNet | https://arxiv.org/abs/2312.00360v2 | Mean IoU | 59.3 |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | HDBFormer | https://arxiv.org/abs/2504.13579v1 | Mean IoU | 59.3% |
10-shot image generation > Semantic Segmentation | NYU Depth v2 | EMSANet (2x ResNet-34 NBt1D, PanopticNDT version, finetuned) | https://arxiv.org/abs/2309.13635v2 | Mean IoU | 59.02 |
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