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 | ADE20K val | DCNAS | https://arxiv.org/abs/2003.11883v2 | mIoU | 47.12 |
10-shot image generation > Semantic Segmentation | ADE20K val | ResNeSt-101 | https://arxiv.org/abs/2004.08955v2 | mIoU | 46.91 |
10-shot image generation > Semantic Segmentation | ADE20K val | Seg-S-Mask/16 (MS, ViT-S) | null | mIoU | 46.9 |
10-shot image generation > Semantic Segmentation | ADE20K val | Swin-S (RPE w/ GAB) | https://arxiv.org/abs/2305.04722v1 | mIoU | 46.41 |
10-shot image generation > Semantic Segmentation | ADE20K val | DaViT-B (UperNet) | https://arxiv.org/abs/2204.03645v1 | mIoU | 46.3 |
10-shot image generation > Semantic Segmentation | ADE20K val | CPN(ResNet-101) | https://arxiv.org/abs/2004.01547v1 | mIoU | 46.27 |
10-shot image generation > Semantic Segmentation | ADE20K val | MultiMAE (ViT-B) | https://arxiv.org/abs/2204.01678v1 | mIoU | 46.2 |
10-shot image generation > Semantic Segmentation | ADE20K val | PyConvSegNet-152 | https://arxiv.org/abs/2006.11538v1 | mIoU | 45.99 |
10-shot image generation > Semantic Segmentation | ADE20K val | PyConvSegNet-152 | https://arxiv.org/abs/2006.11538v1 | Pixel Accuracy | 82.49 |
10-shot image generation > Semantic Segmentation | ADE20K val | DNL | https://arxiv.org/abs/2006.06668v2 | mIoU | 45.97 |
10-shot image generation > Semantic Segmentation | ADE20K val | CTNet | https://arxiv.org/abs/2104.09805v1 | mIoU | 45.94 |
10-shot image generation > Semantic Segmentation | ADE20K val | ACNet (ResNet-101) | https://arxiv.org/abs/1911.01664v1 | mIoU | 45.90 |
10-shot image generation > Semantic Segmentation | ADE20K val | ACNet(ResNet-101) | https://arxiv.org/abs/1911.01664v1 | mIoU | 45.90 |
10-shot image generation > Semantic Segmentation | ADE20K val | OCR (HRNetV2-W48) | https://arxiv.org/abs/1909.11065v6 | mIoU | 45.66 |
10-shot image generation > Semantic Segmentation | ADE20K val | EANet (ResNet-101) | https://arxiv.org/abs/2105.02358v2 | mIoU | 45.33 |
10-shot image generation > Semantic Segmentation | ADE20K val | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | mIoU | 45.28 |
10-shot image generation > Semantic Segmentation | ADE20K val | Asymmetric ALNN | https://arxiv.org/abs/1908.07678v5 | mIoU | 45.24 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-VT | https://arxiv.org/abs/2208.11718v1 | mIoU | 45.07 |
10-shot image generation > Semantic Segmentation | ADE20K val | gSwin-VT | https://arxiv.org/abs/2208.11718v1 | Pixel Accuracy | 81.79 |
10-shot image generation > Semantic Segmentation | ADE20K val | LaU-regression-loss | https://arxiv.org/abs/1911.05250v2 | mIoU | 45.02 |
10-shot image generation > Semantic Segmentation | ADE20K val | EncNet (ResNet-101) | http://arxiv.org/abs/1803.08904v1 | mIoU | 44.65 |
10-shot image generation > Semantic Segmentation | ADE20K val | SGR (ResNet-101) | http://papers.nips.cc/paper/7456-symbolic-graph-reasoning-meets-convolutions | mIoU | 44.32 |
10-shot image generation > Semantic Segmentation | ADE20K val | Auto-DeepLab-L | http://arxiv.org/abs/1901.02985v2 | mIoU | 43.98 |
10-shot image generation > Semantic Segmentation | ADE20K val | Auto-DeepLab-L | http://arxiv.org/abs/1901.02985v2 | Pixel Accuracy | 81.72 |
10-shot image generation > Semantic Segmentation | ADE20K val | PSANet (ResNet-101) | http://openaccess.thecvf.com/content_ECCV_2018/html/Hengshuang_Zhao_PSANet_Point-wise_Spatial_ECCV_2018_paper.html | mIoU | 43.77 |
10-shot image generation > Semantic Segmentation | ADE20K val | DSSPN (ResNet-101) | http://arxiv.org/abs/1803.06067v1 | mIoU | 43.68 |
10-shot image generation > Semantic Segmentation | ADE20K val | PSPNet (ResNet-152) | http://arxiv.org/abs/1612.01105v2 | mIoU | 43.51% |
10-shot image generation > Semantic Segmentation | ADE20K val | PSPNet (ResNet-101) | http://arxiv.org/abs/1612.01105v2 | mIoU | 43.29% |
10-shot image generation > Semantic Segmentation | ADE20K val | HRNetV2 (HRNetV2-W48) | http://arxiv.org/abs/1904.04514v1 | mIoU | 42.99 |
10-shot image generation > Semantic Segmentation | ADE20K val | UperNet (ResNet-101) | http://arxiv.org/abs/1807.10221v1 | mIoU | 42.66 |
10-shot image generation > Semantic Segmentation | ADE20K val | RefineNet (ResNet-152) | http://arxiv.org/abs/1611.06612v3 | mIoU | 40.70 |
10-shot image generation > Semantic Segmentation | ADE20K val | RefineNet (ResNet-101) | http://arxiv.org/abs/1611.06612v3 | mIoU | 40.20 |
10-shot image generation > Semantic Segmentation | LaRS | SWIM^2 (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | F1 | 79.9 |
10-shot image generation > Semantic Segmentation | LaRS | SWIM^2 (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | μ | 79.7 |
10-shot image generation > Semantic Segmentation | LaRS | SWIM^2 (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | mIoU | 97.8 |
10-shot image generation > Semantic Segmentation | LaRS | SWIM^2 (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | Q | 78.1 |
10-shot image generation > Semantic Segmentation | LaRS | TransMari (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | F1 | 80.2 |
10-shot image generation > Semantic Segmentation | LaRS | TransMari (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | μ | 79.6 |
10-shot image generation > Semantic Segmentation | LaRS | TransMari (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | mIoU | 97.1 |
10-shot image generation > Semantic Segmentation | LaRS | TransMari (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | Q | 77.8 |
10-shot image generation > Semantic Segmentation | LaRS | Mari-Mask2Former (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | F1 | 77.3 |
10-shot image generation > Semantic Segmentation | LaRS | Mari-Mask2Former (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | μ | 78.4 |
10-shot image generation > Semantic Segmentation | LaRS | Mari-Mask2Former (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | mIoU | 97.8 |
10-shot image generation > Semantic Segmentation | LaRS | Mari-Mask2Former (Mask2Former) | https://arxiv.org/abs/2311.14762v1 | Q | 75.7 |
10-shot image generation > Semantic Segmentation | LaRS | KNet (Swin-T) | https://arxiv.org/abs/2308.09618v1 | F1 | 73.4 |
10-shot image generation > Semantic Segmentation | LaRS | KNet (Swin-T) | https://arxiv.org/abs/2308.09618v1 | μ | 78.8 |
10-shot image generation > Semantic Segmentation | LaRS | KNet (Swin-T) | https://arxiv.org/abs/2308.09618v1 | mIoU | 97.2 |
10-shot image generation > Semantic Segmentation | LaRS | KNet (Swin-T) | https://arxiv.org/abs/2308.09618v1 | Q | 71.3 |
10-shot image generation > Semantic Segmentation | LaRS | SegFormer (MiT-B2) | https://arxiv.org/abs/2308.09618v1 | F1 | 70.0 |
10-shot image generation > Semantic Segmentation | LaRS | SegFormer (MiT-B2) | https://arxiv.org/abs/2308.09618v1 | μ | 78.6 |
10-shot image generation > Semantic Segmentation | LaRS | SegFormer (MiT-B2) | https://arxiv.org/abs/2308.09618v1 | mIoU | 96.8 |
10-shot image generation > Semantic Segmentation | LaRS | SegFormer (MiT-B2) | https://arxiv.org/abs/2308.09618v1 | Q | 67.8 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3 (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | F1 | 66.1 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3 (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | μ | 77.5 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3 (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | mIoU | 95.2 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3 (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | Q | 62.9 |
10-shot image generation > Semantic Segmentation | LaRS | PointRend | https://arxiv.org/abs/2308.09618v1 | F1 | 65.4 |
10-shot image generation > Semantic Segmentation | LaRS | PointRend | https://arxiv.org/abs/2308.09618v1 | μ | 77.5 |
10-shot image generation > Semantic Segmentation | LaRS | PointRend | https://arxiv.org/abs/2308.09618v1 | mIoU | 94.9 |
10-shot image generation > Semantic Segmentation | LaRS | PointRend | https://arxiv.org/abs/2308.09618v1 | Q | 62.1 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3+ (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | F1 | 64.0 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3+ (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | μ | 77.8 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3+ (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | mIoU | 95.4 |
10-shot image generation > Semantic Segmentation | LaRS | DeepLabv3+ (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | Q | 61.0 |
10-shot image generation > Semantic Segmentation | LaRS | STDC2 | https://arxiv.org/abs/2308.09618v1 | F1 | 64.3 |
10-shot image generation > Semantic Segmentation | LaRS | STDC2 | https://arxiv.org/abs/2308.09618v1 | μ | 76.5 |
10-shot image generation > Semantic Segmentation | LaRS | STDC2 | https://arxiv.org/abs/2308.09618v1 | mIoU | 94.5 |
10-shot image generation > Semantic Segmentation | LaRS | STDC2 | https://arxiv.org/abs/2308.09618v1 | Q | 60.8 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | F1 | 63.4 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | μ | 77.4 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | mIoU | 95.0 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | Q | 60.2 |
10-shot image generation > Semantic Segmentation | LaRS | WaSR (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | F1 | 61.6 |
10-shot image generation > Semantic Segmentation | LaRS | WaSR (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | μ | 71.0 |
10-shot image generation > Semantic Segmentation | LaRS | WaSR (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | mIoU | 96.6 |
10-shot image generation > Semantic Segmentation | LaRS | WaSR (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | Q | 59.5 |
10-shot image generation > Semantic Segmentation | LaRS | STDC1 | https://arxiv.org/abs/2308.09618v1 | F1 | 61.8 |
10-shot image generation > Semantic Segmentation | LaRS | STDC1 | https://arxiv.org/abs/2308.09618v1 | μ | 75.6 |
10-shot image generation > Semantic Segmentation | LaRS | STDC1 | https://arxiv.org/abs/2308.09618v1 | mIoU | 93.6 |
10-shot image generation > Semantic Segmentation | LaRS | STDC1 | https://arxiv.org/abs/2308.09618v1 | Q | 57.8 |
10-shot image generation > Semantic Segmentation | LaRS | eWaSR | https://arxiv.org/abs/2311.14762v1 | F1 | 58.9 |
10-shot image generation > Semantic Segmentation | LaRS | eWaSR | https://arxiv.org/abs/2311.14762v1 | μ | 67.8 |
10-shot image generation > Semantic Segmentation | LaRS | eWaSR | https://arxiv.org/abs/2311.14762v1 | mIoU | 96.0 |
10-shot image generation > Semantic Segmentation | LaRS | eWaSR | https://arxiv.org/abs/2311.14762v1 | Q | 56.5 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | F1 | 57.9 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | μ | 76.8 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | mIoU | 92.6 |
10-shot image generation > Semantic Segmentation | LaRS | FCN (ResNet-50) | https://arxiv.org/abs/2308.09618v1 | Q | 53.6 |
10-shot image generation > Semantic Segmentation | LaRS | Segmenter (ViT-B) | https://arxiv.org/abs/2308.09618v1 | F1 | 55.2 |
10-shot image generation > Semantic Segmentation | LaRS | Segmenter (ViT-B) | https://arxiv.org/abs/2308.09618v1 | μ | 72.2 |
10-shot image generation > Semantic Segmentation | LaRS | Segmenter (ViT-B) | https://arxiv.org/abs/2308.09618v1 | mIoU | 95.1 |
10-shot image generation > Semantic Segmentation | LaRS | Segmenter (ViT-B) | https://arxiv.org/abs/2308.09618v1 | Q | 52.6 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv2 | https://arxiv.org/abs/2308.09618v1 | F1 | 54.7 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv2 | https://arxiv.org/abs/2308.09618v1 | μ | 73.9 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv2 | https://arxiv.org/abs/2308.09618v1 | mIoU | 93.5 |
10-shot image generation > Semantic Segmentation | LaRS | BiSeNetv2 | https://arxiv.org/abs/2308.09618v1 | Q | 51.2 |
10-shot image generation > Semantic Segmentation | LaRS | WODIS (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | F1 | 47.5 |
10-shot image generation > Semantic Segmentation | LaRS | WODIS (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | μ | 63.0 |
10-shot image generation > Semantic Segmentation | LaRS | WODIS (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | mIoU | 85.7 |
10-shot image generation > Semantic Segmentation | LaRS | WODIS (ResNet-101) | https://arxiv.org/abs/2308.09618v1 | Q | 40.7 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.