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 | BDD100K val | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 62.3 |
10-shot image generation > Semantic Segmentation | BDD100K val | EMANet | https://arxiv.org/abs/1907.13426v2 | mIoU | 61.4 |
10-shot image generation > Semantic Segmentation | BDD100K val | OCRNet | https://arxiv.org/abs/1909.11065v6 | mIoU | 60.1 |
10-shot image generation > Semantic Segmentation | BDD100K val | NiseNet | https://ieeexplore.ieee.org/document/8803299 | mIoU | 53.52 |
10-shot image generation > Semantic Segmentation | BDD100K val | MRFP+(Ours) Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 39.55 |
10-shot image generation > Semantic Segmentation | BDD100K val | Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 31.44 |
10-shot image generation > Semantic Segmentation | BDD100K val | BiSeNet-V1(ResNet-18) | http://arxiv.org/abs/1808.00897v1 | mIoU | 53.8(45.1fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | ICNet | http://arxiv.org/abs/1704.08545v2 | mIoU | 52.4(39.5fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | Bi-Align | https://arxiv.org/abs/2105.11651v1 | mIoU | 53.4(42.1fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | DF1-Seg | http://arxiv.org/abs/1903.03777v2 | mIoU | 42.5(82.3fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | DF2-Seg | http://arxiv.org/abs/1903.03777v2 | mIoU | 47.8(53.4fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | STDC1 | https://arxiv.org/abs/2104.13188v1 | mIoU | 52.1(45.8FPS) |
10-shot image generation > Semantic Segmentation | BDD100K val | STDC2 | https://arxiv.org/abs/2104.13188v1 | mIoU | 53.8(33.0FPS) |
10-shot image generation > Semantic Segmentation | BDD100K val | SFNet(DF2) | https://arxiv.org/abs/2002.10120v3 | mIoU | 60.2(208FPS 4090) |
10-shot image generation > Semantic Segmentation | BDD100K val | SFNet(DF1) | https://arxiv.org/abs/2002.10120v3 | mIoU | 55.4(70.3fps) |
10-shot image generation > Semantic Segmentation | BDD100K val | SFNet(ResNet-18) | https://arxiv.org/abs/2002.10120v3 | mIoU | 60.6(132.5FPS 4090) |
10-shot image generation > Semantic Segmentation | BDD100K val | SFNet-Lite(ResNet-18) | https://arxiv.org/abs/2207.04415v1 | mIoU | 60.6(161.3FPS 4090) |
10-shot image generation > Semantic Segmentation | BDD100K val | SFNet-Lite(STDC2) | https://arxiv.org/abs/2207.04415v1 | mIoU | 60.6(194.5FPS 4090) |
10-shot image generation > Semantic Segmentation | BDD100K val | DSNet-head64 | https://arxiv.org/abs/2406.03702v1 | mIoU | 62.6(172.2FPS 4090) |
10-shot image generation > Semantic Segmentation | SELMA | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 91.7 |
10-shot image generation > Semantic Segmentation | SELMA | SegFormer | https://arxiv.org/abs/2105.15203v3 | mIoU | 77.2 |
10-shot image generation > Semantic Segmentation | SELMA | DeepLabV3 | http://arxiv.org/abs/1706.05587v3 | mIoU | 70.7 |
10-shot image generation > Semantic Segmentation | SELMA | DeepLabV2 | http://arxiv.org/abs/1606.00915v2 | mIoU | 68.9 |
10-shot image generation > Semantic Segmentation | SELMA | PSPNet | http://arxiv.org/abs/1612.01105v2 | mIoU | 68.4 |
10-shot image generation > Semantic Segmentation | SELMA | FCN | http://arxiv.org/abs/1411.4038v2 | mIoU | 68.2 |
10-shot image generation > Semantic Segmentation | SELMA | UNet | http://arxiv.org/abs/1505.04597v1 | mIoU | 36.2 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | RoadFormer+ (ConvNeXt-L, RGB-AoLP) | https://arxiv.org/abs/2407.21631v2 | mIoU | 93.0 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | RoadFormer+ (ConvNeXt-B, RGB-AoLP) | https://arxiv.org/abs/2407.21631v2 | mIoU | 92.9 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | ShareCMP (B4 RGB-FP) | https://arxiv.org/abs/2312.03430v2 | mIoU | 92.7 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CMX (B4 RGB-AoLP) | https://arxiv.org/abs/2203.04838v5 | mIoU | 92.6 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | ShareCMP (B2 RGB-FP) | https://arxiv.org/abs/2312.03430v2 | mIoU | 92.4 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CMX (B2 RGB-DoLP) | https://arxiv.org/abs/2203.04838v5 | mIoU | 92.2 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CSFNet-2 | https://arxiv.org/abs/2407.01328v1 | mIoU | 91.40 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CSFNet-2 | https://arxiv.org/abs/2407.01328v1 | Frame (fps) | 75 (3090) |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CSFNet-1 | https://arxiv.org/abs/2407.01328v1 | mIoU | 90.85 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | CSFNet-1 | https://arxiv.org/abs/2407.01328v1 | Frame (fps) | 108.5 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | SegFormer-B2 (RGB) | https://arxiv.org/abs/2105.15203v3 | mIoU | 89.6 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | EAFNet (RGB-AoLP) | https://arxiv.org/abs/2011.13313v2 | mIoU | 85.7 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | EAFNet (RGB-DoLP) | https://arxiv.org/abs/2011.13313v2 | mIoU | 85.4 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | EAFNet (3Path) | https://arxiv.org/abs/2011.13313v2 | mIoU | 83.4 |
10-shot image generation > Semantic Segmentation | ZJU-RGB-P | SwiftNet (RGB) | http://arxiv.org/abs/1903.08469v2 | mIoU | 80.3 |
10-shot image generation > Semantic Segmentation | HePIC 🏛️ | BIM-Net++ | https://ieeexplore.ieee.org/document/10222064 | mIoU | 43.7 |
10-shot image generation > Semantic Segmentation | HePIC 🏛️ | BIM-Net | https://ieeexplore.ieee.org/document/10222064 | mIoU | 40.6 |
10-shot image generation > Semantic Segmentation | SWINSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Precision | 0.917 |
10-shot image generation > Semantic Segmentation | SWINSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Average Recall | 0.982 |
10-shot image generation > Semantic Segmentation | SWINSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | F1-Score | 0.947 |
10-shot image generation > Semantic Segmentation | SWINSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | Mean IoU | 0.985 |
10-shot image generation > Semantic Segmentation | SWINSEG | ACLNet | https://arxiv.org/abs/2207.06277v1 | MCC | 0.930 |
10-shot image generation > Semantic Segmentation | PH2 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | Average Dice | 95.70 |
10-shot image generation > Semantic Segmentation | PH2 | MobileUNETR | https://arxiv.org/abs/2409.03062v1 | Average IOU | 92.30 |
10-shot image generation > Semantic Segmentation | PH2 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Average Dice | 95.4 |
10-shot image generation > Semantic Segmentation | PH2 | MFSNet | https://arxiv.org/abs/2203.14341v2 | Average IOU | 0.914 |
10-shot image generation > Semantic Segmentation | DDD17 | BRENet | https://arxiv.org/abs/2505.01548v1 | mIoU | 78.56 |
10-shot image generation > Semantic Segmentation | DDD17 | CMNeXt | https://arxiv.org/abs/2303.01480v1 | mIoU | 72.67 |
10-shot image generation > Semantic Segmentation | DDD17 | CMX | https://arxiv.org/abs/2203.04838v5 | mIoU | 71.88 |
10-shot image generation > Semantic Segmentation | DDD17 | SegNeXt-B | https://arxiv.org/abs/2209.08575v1 | mIoU | 71.46 |
10-shot image generation > Semantic Segmentation | DDD17 | SegFormer-B2 | https://arxiv.org/abs/2105.15203v3 | mIoU | 71.05 |
10-shot image generation > Semantic Segmentation | DDD17 | EDCNet-S2D | https://arxiv.org/abs/2112.05006v1 | mIoU | 61.99 |
10-shot image generation > Semantic Segmentation | DDD17 | ESS | https://arxiv.org/abs/2203.10016v2 | mIoU | 61.37 |
10-shot image generation > Semantic Segmentation | DDD17 | HALSIE | https://arxiv.org/abs/2211.10754v4 | mIoU | 60.66 |
10-shot image generation > Semantic Segmentation | DDD17 | EV-SegNet | http://arxiv.org/abs/1811.12039v1 | mIoU | 54.81 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+HHA) | https://arxiv.org/abs/2308.09369v1 | mIoU | 60.6% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+HHA) | https://arxiv.org/abs/2308.09369v1 | mAcc | 70.68 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | mIoU | 59.43% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | mAcc | 69.03 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | mIoU | 58.24% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | mAcc | 68.79 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SGAT4PASS（RGB only, Fold 1） | https://arxiv.org/abs/2306.03403v2 | mIoU | 56.4% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | mIoU | 55.49% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | mAcc | 68.57 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SGAT4PASS（RGB only, 3 Fold AVG） | https://arxiv.org/abs/2306.03403v2 | mIoU | 55.3% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS+ (Supervised + Small + MS) | https://arxiv.org/abs/2207.11860v5 | mIoU | 54.0% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS (Supervised + Small + MS) | https://arxiv.org/abs/2203.01452v2 | mIoU | 53.0% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | mIoU | 52.87% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | mAcc | 63.96 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS+ (UDA + MPA + MS) | https://arxiv.org/abs/2207.11860v5 | mIoU | 52.3% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | CBFC | https://arxiv.org/abs/2207.02437v1 | mIoU | 52.2% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | CBFC | https://arxiv.org/abs/2207.02437v1 | mAcc | 65.6 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS (Supervised + Small) | https://arxiv.org/abs/2203.01452v2 | mIoU | 52.1% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | HoHoNet (ResNet-101) | https://arxiv.org/abs/2011.11498v3 | mIoU | 52.0% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | HoHoNet (ResNet-101) | https://arxiv.org/abs/2011.11498v3 | mAcc | 65.0 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS (UDA + MPA + MS) | https://arxiv.org/abs/2203.01452v2 | mIoU | 51.2% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS (UDA + MPA) | https://arxiv.org/abs/2203.01452v2 | mIoU | 50.8% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | PanoFormer | https://arxiv.org/abs/2203.09283v2 | mIoU | 48.9% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | PanoFormer | https://arxiv.org/abs/2203.09283v2 | mAcc | 64.5 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Trans4PASS (UDA + Source Only) | https://arxiv.org/abs/2203.01452v2 | mIoU | 48.1% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | FreDSNet | https://arxiv.org/abs/2210.01595v2 | mIoU | 46.1% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | FreDSNet | https://arxiv.org/abs/2210.01595v2 | mAcc | 63.1 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Tangent (ResNet-101) | https://arxiv.org/abs/1912.09390v3 | mIoU | 45.6% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Tangent (ResNet-101) | https://arxiv.org/abs/1912.09390v3 | mAcc | 65.2 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | SWSCNN | https://arxiv.org/abs/2006.10731v2 | mIoU | 43.4% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | HexRUNet | https://arxiv.org/abs/1907.12849v1 | mIoU | 43.3% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | HexRUNet | https://arxiv.org/abs/1907.12849v1 | mAcc | 58.6 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | Interpolated SelectionConv | https://arxiv.org/abs/2210.10123v1 | mIoU | 41.4% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | GaugeNet (R2R-U-Net) | https://arxiv.org/abs/1902.04615v3 | mIoU | 39.4% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | GaugeNet (R2R-U-Net) | https://arxiv.org/abs/1902.04615v3 | mAcc | 55.9 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | UGSCNN | http://arxiv.org/abs/1901.02039v1 | mIoU | 38.3% |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | UGSCNN | http://arxiv.org/abs/1901.02039v1 | mAcc | 54.65 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic | DisConv | http://openaccess.thecvf.com/content_ECCV_2018/html/Keisuke_Tateno_Distortion-Aware_Convolutional_Filters_ECCV_2018_paper.html | mIoU | 34.6% |
10-shot image generation > Semantic Segmentation | MixedWM38 | WaferSegClassNet | https://arxiv.org/abs/2207.00960v1 | Dice | 0.9999 |
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