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 | MixedWM38 | WaferSegClassNet | https://arxiv.org/abs/2207.00960v1 | Mean IoU | 0.9999 |
10-shot image generation > Semantic Segmentation | SWIMSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Precision | 0.964 |
10-shot image generation > Semantic Segmentation | SWIMSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Recall | 0.979 |
10-shot image generation > Semantic Segmentation | SWIMSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | F1-Score | 0.971 |
10-shot image generation > Semantic Segmentation | SWIMSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Mean IoU | 0.992 |
10-shot image generation > Semantic Segmentation | SWIMSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | MCC | 0.956 |
10-shot image generation > Semantic Segmentation | SYNTHIA | CGA-Net | http://openaccess.thecvf.com//content/CVPR2021/html/Lu_CGA-Net_Category_Guided_Aggregation_for_Point_Cloud_Semantic_Segmentation_CVPR_2021_paper.html | mIoU | 83.8 |
10-shot image generation > Semantic Segmentation | SYNTHIA | MRFP+(Ours) Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 30.22 |
10-shot image generation > Semantic Segmentation | SYNTHIA | Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 25.84 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | MIC | https://arxiv.org/abs/2212.01322v2 | mIoU | 75.9 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | HRDA + PiPa | https://arxiv.org/abs/2211.07609v1 | mIoU | 75.6 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | HRDA | https://arxiv.org/abs/2204.13132v2 | mIoU | 73.8 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | SePiCo | https://arxiv.org/abs/2204.08808v2 | mIoU | 70.3 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | DAFormer + ProCST | https://arxiv.org/abs/2204.11891v2 | mIoU | 69.4 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | DAFormer | https://arxiv.org/abs/2111.14887v2 | mIoU | 68.3 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | TransDA-B | https://arxiv.org/abs/2203.07988v1 | mIoU | 63.9 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | G2L | https://www.sciencedirect.com/science/article/pii/S1877050922012170 | mIoU | 59.7 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | ProDA+CRA | https://arxiv.org/abs/2109.06422v2 | mIoU | 58.6 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | ProDA | https://arxiv.org/abs/2101.10979v2 | mIoU | 57.5 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | CMFormer | https://arxiv.org/abs/2307.00371v5 | mIoU | 55.3 |
10-shot image generation > Semantic Segmentation | GTAV-to-Cityscapes Labels | CCM | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2178_ECCV_2020_paper.php | mIoU | 49.9 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | Bimodal SegNet | https://arxiv.org/abs/2303.11228v2 | mIoU | 87.05 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 85.81 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU | 84.08 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | DeepLab | http://arxiv.org/abs/1606.00915v2 | mIoU | 71.05 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | U-Net | http://arxiv.org/abs/1505.04597v1 | mIoU | 64.7 |
10-shot image generation > Semantic Segmentation | Event-based Segmentation Dataset | FCN | http://arxiv.org/abs/1411.4038v2 | mIoU | 59.6 |
10-shot image generation > Semantic Segmentation | SYNTHIA-CVPR’16 | SSMA | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 92.1 |
10-shot image generation > Semantic Segmentation | SYNTHIA-CVPR’16 | AdapNet++ | https://arxiv.org/abs/1808.03833v3 | Mean IoU | 87.87 |
10-shot image generation > Semantic Segmentation | ADE20K val | BEiT-3 | https://arxiv.org/abs/2208.10442v2 | mIoU | 62.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | ViT-CoMer | https://openreview.net/forum?id=srPMwpkWbR&invitationId=thecvf.com/CVPR/2024/Conference/Submission1221/-/Camera_Ready_Revision&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3Dthecvf.com%2FCVPR%2F2024%2FConference%2FAuthors%23author-tasks) | mIoU | 62.1 |
10-shot image generation > Semantic Segmentation | ADE20K val | EVA | https://arxiv.org/abs/2211.07636v2 | mIoU | 61.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | FD-SwinV2-G | https://arxiv.org/abs/2205.14141v3 | mIoU | 61.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | MaskDINO-SwinL | https://arxiv.org/abs/2206.02777v3 | mIoU | 60.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | OneFormer (InternImage-H, emb_dim=256, multi-scale, 896x896) | https://arxiv.org/abs/2211.06220v2 | mIoU | 60.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | ViT-Adapter-L (Mask2Former, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | mIoU | 60.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | SERNet-Former_v2 | https://arxiv.org/abs/2401.15741v7 | mIoU | 59.35 |
10-shot image generation > Semantic Segmentation | ADE20K val | OneFormer (DiNAT-L, multi-scale, 896x896) | https://arxiv.org/abs/2211.06220v2 | mIoU | 58.6 |
10-shot image generation > Semantic Segmentation | ADE20K val | ViT-Adapter-L (UperNet, BEiT pretrain) | https://arxiv.org/abs/2205.08534v4 | mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | OneFormer (DiNAT-L, multi-scale, 640x640) | https://arxiv.org/abs/2211.06220v2 | mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | RSSeg-ViT-L(BEiT pretrain) | https://arxiv.org/abs/2212.13764v1 | mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | EoMT (DINOv2-L, single-scale, 512x512) | https://arxiv.org/abs/2503.19108v1 | mIoU | 58.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | OneFormer (Swin-L, multi-scale, 896x896) | https://arxiv.org/abs/2211.06220v2 | mIoU | 58.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L FaPN-Mask2Former) | https://arxiv.org/abs/2112.12782v3 | mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L MSFaPN-Mask2Former) | https://arxiv.org/abs/2112.12782v3 | mIoU | 58.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | DiNAT-L (Mask2Former) | https://arxiv.org/abs/2209.15001v3 | mIoU | 58.1 |
10-shot image generation > Semantic Segmentation | ADE20K val | Mask2Former (Swin-L-FaPN, multiscale) | https://arxiv.org/abs/2112.01527v3 | mIoU | 57.7 |
10-shot image generation > Semantic Segmentation | ADE20K val | OneFormer (Swin-L, multi-scale, 640x640) | https://arxiv.org/abs/2211.06220v2 | mIoU | 57.7 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L Mask2Former) | https://arxiv.org/abs/2112.12782v3 | mIoU | 57.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | SenFormer (BEiT-L) | https://arxiv.org/abs/2111.13280v2 | mIoU | 57.1 |
10-shot image generation > Semantic Segmentation | ADE20K val | BEiT-L (ViT+UperNet, ImageNet-22k pretrain) | https://arxiv.org/abs/2106.08254v2 | mIoU | 57.0 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L MSFaPN-Mask2Former, single-scale) | https://arxiv.org/abs/2112.12782v3 | mIoU | 57.0 |
10-shot image generation > Semantic Segmentation | ADE20K val | FaPN (MaskFormer, Swin-L, ImageNet-22k pretrain) | https://arxiv.org/abs/2108.07058v2 | mIoU | 56.7 |
10-shot image generation > Semantic Segmentation | ADE20K val | Mask2Former (Swin-L-FaPN) | https://arxiv.org/abs/2112.01527v3 | mIoU | 56.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L MaskFormer) | https://arxiv.org/abs/2112.12782v3 | mIoU | 56.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | CSWin-L (UperNet, ImageNet-22k pretrain) | https://arxiv.org/abs/2107.00652v3 | mIoU | 55.7 |
10-shot image generation > Semantic Segmentation | ADE20K val | MaskFormer (Swin-L, ImageNet-22k pretrain) | https://arxiv.org/abs/2107.06278v2 | mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | ADE20K val | DeiT-L | https://arxiv.org/abs/2204.07118v1 | mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | ADE20K val | Focal-L (UperNet, ImageNet-22k pretrain) | https://arxiv.org/abs/2107.00641v1 | mIoU | 55.4 |
10-shot image generation > Semantic Segmentation | ADE20K val | SegViT ViT-Large | https://arxiv.org/abs/2210.05844v2 | mIoU | 55.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | PatchDiverse + Swin-L (multi-scale test, upernet, ImageNet22k pretrain) | https://arxiv.org/abs/2104.12753v3 | mIoU | 54.4% |
10-shot image generation > Semantic Segmentation | ADE20K val | K-Net | https://arxiv.org/abs/2106.14855v2 | mIoU | 54.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | DEPICT-SA (ViT-L 640x640 multi-scale) | https://arxiv.org/abs/2411.03033v3 | mIoU | 54.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | SenFormer (Swin-L) | https://arxiv.org/abs/2111.13280v2 | mIoU | 54.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | DeiT-B | https://arxiv.org/abs/2204.07118v1 | mIoU | 54.1 |
10-shot image generation > Semantic Segmentation | ADE20K val | MixMIM-L | https://arxiv.org/abs/2205.13137v4 | mIoU | 53.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | Seg-L-Mask/16 (MS, ViT-L) | https://arxiv.org/abs/2105.05633v3 | mIoU | 53.63 |
10-shot image generation > Semantic Segmentation | ADE20K val | Swin-L (UperNet, ImageNet-22k pretrain) | https://arxiv.org/abs/2103.14030v2 | mIoU | 53.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | SeMask (SeMask Swin-L FPN) | https://arxiv.org/abs/2112.12782v3 | mIoU | 53.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | PatchConvNet-L120 (UperNet) | https://arxiv.org/abs/2112.13692v1 | mIoU | 52.9 |
10-shot image generation > Semantic Segmentation | ADE20K val | DEPICT-SA (ViT-L 640x640 single-scale) | https://arxiv.org/abs/2411.03033v3 | mIoU | 52.9 |
10-shot image generation > Semantic Segmentation | ADE20K val | PatchConvNet-B120 (UperNet) | https://arxiv.org/abs/2112.13692v1 | mIoU | 52.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | SegFormer-B5(MS, 87M #Params, ImageNet-1K pretrain) | https://arxiv.org/abs/2105.15203v3 | mIoU | 51.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | Light-Ham (VAN-Huge, 61M, IN-1k, MS) | https://arxiv.org/abs/2109.04553v2 | mIoU | 51.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | CrossFormer (ImageNet1k-pretrain, UPerNet, multi-scale test) | https://arxiv.org/abs/2108.00154v2 | mIoU | 51.4% |
10-shot image generation > Semantic Segmentation | ADE20K val | CrossFormer (ImageNet1k-pretrain, UPerNet, multi-scale test) | https://arxiv.org/abs/2108.00154v2 | Pixel Accuracy | 84.0% |
10-shot image generation > Semantic Segmentation | ADE20K val | PatchConvNet-B60 (UperNet) | https://arxiv.org/abs/2112.13692v1 | mIoU | 51.1 |
10-shot image generation > Semantic Segmentation | ADE20K val | Light-Ham (VAN-Large, 46M, IN-1k, MS) | https://arxiv.org/abs/2109.04553v2 | mIoU | 51.0 |
10-shot image generation > Semantic Segmentation | ADE20K val | UperNet Shuffle-B | https://arxiv.org/abs/2106.03650v1 | mIoU | 50.5 |
10-shot image generation > Semantic Segmentation | ADE20K val | ELSA-Swin-S | https://arxiv.org/abs/2112.12786v1 | mIoU | 50.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | MixMIM-B | https://arxiv.org/abs/2205.13137v4 | mIoU | 50.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | Twins-SVT-L (UperNet, ImageNet-1k pretrain) | https://arxiv.org/abs/2104.13840v4 | mIoU | 50.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | Seg-B-Mask/16 (MS, ViT-B) | https://arxiv.org/abs/2105.05633v3 | mIoU | 50.0 |
10-shot image generation > Semantic Segmentation | ADE20K val | Swin-B (UperNet, ImageNet-1k pretrain) | https://arxiv.org/abs/2103.14030v2 | mIoU | 49.7 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-S | https://arxiv.org/abs/2208.11718v1 | mIoU | 49.69 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-S | https://arxiv.org/abs/2208.11718v1 | Pixel Accuracy | 83.43 |
10-shot image generation > Semantic Segmentation | ADE20K val | Seg-B/8 (MS, ViT-B) | https://arxiv.org/abs/2105.05633v3 | mIoU | 49.61 |
10-shot image generation > Semantic Segmentation | ADE20K val | Seg-B/8 (MS, ViT-B) | https://arxiv.org/abs/2105.05633v3 | Pixel Accuracy | 83.37 |
10-shot image generation > Semantic Segmentation | ADE20K val | UperNet Shuffle-S | https://arxiv.org/abs/2106.03650v1 | mIoU | 49.6 |
10-shot image generation > Semantic Segmentation | ADE20K val | Light-Ham (VAN-Base, 27M, IN-1k, MS) | https://arxiv.org/abs/2109.04553v2 | mIoU | 49.6 |
10-shot image generation > Semantic Segmentation | ADE20K val | PatchConvNet-S60 (UperNet) | https://arxiv.org/abs/2112.13692v1 | mIoU | 49.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | DPT-Hybrid | https://arxiv.org/abs/2103.13413v1 | mIoU | 49.02 |
10-shot image generation > Semantic Segmentation | ADE20K val | DPT-Hybrid | https://arxiv.org/abs/2103.13413v1 | Pixel Accuracy | 83.11 |
10-shot image generation > Semantic Segmentation | ADE20K val | DaViT-S (UperNet) | https://arxiv.org/abs/2204.03645v1 | mIoU | 48.8 |
10-shot image generation > Semantic Segmentation | ADE20K val | ResNeSt-200 | https://arxiv.org/abs/2004.08955v2 | mIoU | 48.36 |
10-shot image generation > Semantic Segmentation | ADE20K val | HRNetV2 + OCR + RMI (PaddleClas pretrained) | https://arxiv.org/abs/1909.11065v6 | mIoU | 47.98 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-T | https://arxiv.org/abs/2208.11718v1 | mIoU | 47.63 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-T | https://arxiv.org/abs/2208.11718v1 | Pixel Accuracy | 82.60 |
10-shot image generation > Semantic Segmentation | ADE20K val | ResNeSt-269 | https://arxiv.org/abs/2004.08955v2 | mIoU | 47.60 |
10-shot image generation > Semantic Segmentation | ADE20K val | UperNet Shuffle-T | https://arxiv.org/abs/2106.03650v1 | mIoU | 47.6 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.