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 | ScanNet | MinkowskiNet | https://arxiv.org/abs/1904.08755v4 | val mIoU | 72.2 |
10-shot image generation > Semantic Segmentation | ScanNet | SparseConvNet | http://arxiv.org/abs/1711.10275v1 | test mIoU | 72.5 |
10-shot image generation > Semantic Segmentation | ScanNet | SparseConvNet | http://arxiv.org/abs/1711.10275v1 | val mIoU | 69.3 |
10-shot image generation > Semantic Segmentation | ScanNet | KpConv | https://arxiv.org/abs/1904.08889v2 | test mIoU | 68.0 |
10-shot image generation > Semantic Segmentation | ScanNet | KpConv | https://arxiv.org/abs/1904.08889v2 | val mIoU | 69.2 |
10-shot image generation > Semantic Segmentation | ScanNet | PanopticNDT (10cm) | https://arxiv.org/abs/2309.13635v2 | test mIoU | 68.1 |
10-shot image generation > Semantic Segmentation | ScanNet | PanopticNDT (10cm) | https://arxiv.org/abs/2309.13635v2 | val mIoU | 68.39 |
10-shot image generation > Semantic Segmentation | ScanNet | PointConv | https://arxiv.org/abs/1811.07246v3 | test mIoU | 55.6 |
10-shot image generation > Semantic Segmentation | ScanNet | PointConv | https://arxiv.org/abs/1811.07246v3 | val mIoU | 61.0 |
10-shot image generation > Semantic Segmentation | ScanNet | PointNet++ | http://arxiv.org/abs/1706.02413v1 | test mIoU | 33.9 |
10-shot image generation > Semantic Segmentation | ScanNet | PointNet++ | http://arxiv.org/abs/1706.02413v1 | val mIoU | 53.5 |
10-shot image generation > Semantic Segmentation | ScanNet | VMVF | https://arxiv.org/abs/2007.13138v1 | test mIoU | 74.6 |
10-shot image generation > Semantic Segmentation | ScanNet | FG-Net | https://arxiv.org/abs/2012.09439v2 | test mIoU | 69.0 |
10-shot image generation > Semantic Segmentation | ScanNet | RPNet | https://arxiv.org/abs/2108.12468v1 | test mIoU | 68.2 |
10-shot image generation > Semantic Segmentation | ScanNet | SAFNet | https://arxiv.org/abs/2107.01579v3 | test mIoU | 65.4 |
10-shot image generation > Semantic Segmentation | ScanNet | TextureNet | http://arxiv.org/abs/1812.00020v2 | test mIoU | 56.6 |
10-shot image generation > Semantic Segmentation | ScanNet | PanopticFusion | https://arxiv.org/abs/1903.01177v2 | test mIoU | 52.9 |
10-shot image generation > Semantic Segmentation | ScanNet | 3DMV | http://arxiv.org/abs/1803.10409v1 | test mIoU | 48.4 |
10-shot image generation > Semantic Segmentation | ScanNet | PointCNN | http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points | test mIoU | 45.8 |
10-shot image generation > Semantic Segmentation | ScanNet | FCPN | http://arxiv.org/abs/1808.06840v1 | test mIoU | 44.7 |
10-shot image generation > Semantic Segmentation | ScanNet | SurfaceConvPF | https://arxiv.org/abs/1808.04952v2 | test mIoU | 44.2 |
10-shot image generation > Semantic Segmentation | ScanNet | Tangent Convolutions | http://arxiv.org/abs/1807.02443v1 | test mIoU | 44.2 |
10-shot image generation > Semantic Segmentation | ScanNet | SPLAT Net | http://arxiv.org/abs/1802.08275v4 | test mIoU | 39.3 |
10-shot image generation > Semantic Segmentation | ScanNet | ScanNet | http://arxiv.org/abs/1702.04405v2 | test mIoU | 30.6 |
10-shot image generation > Semantic Segmentation | Dark Zurich | Refign (HRDA) | https://arxiv.org/abs/2207.06825v3 | mIoU | 63.9 |
10-shot image generation > Semantic Segmentation | Dark Zurich | CoDA | https://arxiv.org/abs/2403.17369v3 | mIoU | 61.2 |
10-shot image generation > Semantic Segmentation | Dark Zurich | MIC | https://arxiv.org/abs/2212.01322v2 | mIoU | 60.2 |
10-shot image generation > Semantic Segmentation | Dark Zurich | Refign (DAFormer) | https://arxiv.org/abs/2207.06825v3 | mIoU | 56.2 |
10-shot image generation > Semantic Segmentation | Dark Zurich | HRDA | https://arxiv.org/abs/2204.13132v2 | mIoU | 55.9 |
10-shot image generation > Semantic Segmentation | Dark Zurich | SePiCo | https://arxiv.org/abs/2204.08808v2 | mIoU | 54.2 |
10-shot image generation > Semantic Segmentation | Dark Zurich | DAFormer | https://arxiv.org/abs/2111.14887v2 | mIoU | 53.8 |
10-shot image generation > Semantic Segmentation | Dark Zurich | GPS-GLASS | https://arxiv.org/abs/2207.13297v5 | mIoU | 46.4 |
10-shot image generation > Semantic Segmentation | Dark Zurich | SePiCo (DeepLab v2 ResNet-101) | https://arxiv.org/abs/2204.08808v2 | mIoU | 45.4 |
10-shot image generation > Semantic Segmentation | Dark Zurich | MGCDA | https://arxiv.org/abs/2005.14553v2 | mIoU | 42.5 |
10-shot image generation > Semantic Segmentation | Dark Zurich | DANNet (DeepLab v2 ResNet-101) | https://arxiv.org/abs/2104.10834v1 | mIoU | 42.5 |
10-shot image generation > Semantic Segmentation | Dark Zurich | GCMA | https://arxiv.org/abs/1901.05946v2 | mIoU | 42.0 |
10-shot image generation > Semantic Segmentation | Dark Zurich | DANNet | https://arxiv.org/abs/2104.10834v1 | mIoU | 36.76 |
10-shot image generation > Semantic Segmentation | Dark Zurich | CIConv | https://arxiv.org/abs/2108.05137v2 | mIoU | 34.5 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | HyperSeg | https://arxiv.org/abs/2411.17606v2 | mIoU | 77.2 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | ViT-P (OneFormer, InternImage-H) | https://arxiv.org/abs/2505.19795v1 | mIoU | 69.1 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | OneFormer (InternImage-H, emb_dim=1024, single-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 68.8 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | ViT-P (OneFormer, DiNAT-L) | https://arxiv.org/abs/2505.19795v1 | mIoU | 68.8 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | OneFormer (DiNAT-L, single-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 68.1 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | OneFormer (Swin-L, single-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 67.4 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | Mask2Former (Swin-L, single-scale) | https://arxiv.org/abs/2112.01527v3 | mIoU | 67.4 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | MaskFormer (Swin-L, single-scale) | https://arxiv.org/abs/2112.01527v3 | mIoU | 64.8 |
10-shot image generation > Semantic Segmentation | COCO (Common Objects in Context) | SegCLIP | https://arxiv.org/abs/2211.14813v2 | mIoU | 26.5 |
10-shot image generation > Semantic Segmentation | MUSES: MUlti-SEnsor Semantic perception dataset | CAFuser (Swin-T) | https://arxiv.org/abs/2410.10791v2 | mIoU | 78.2 |
10-shot image generation > Semantic Segmentation | MUSES: MUlti-SEnsor Semantic perception dataset | Mask2Former (Swin-T) | https://arxiv.org/abs/2401.12761v4 | mIoU | 70.74 |
10-shot image generation > Semantic Segmentation | Cityscapes | SPFNet34M | https://arxiv.org/abs/2206.07298v3 | mIoU | 77.8 |
10-shot image generation > Semantic Segmentation | Cityscapes | DiffSeg (512) | https://arxiv.org/abs/2308.12469v3 | mIoU | 21.2 |
10-shot image generation > Semantic Segmentation | Cityscapes | DiffSeg (512) | https://arxiv.org/abs/2308.12469v3 | Pixel Accuracy | 76 |
10-shot image generation > Semantic Segmentation | LIP val | Hulk(Finetune, ViT-L) | https://arxiv.org/abs/2312.01697v4 | mIoU | 66.02% |
10-shot image generation > Semantic Segmentation | LIP val | Hulk(Finetune, ViT-B) | https://arxiv.org/abs/2312.01697v4 | mIoU | 63.98% |
10-shot image generation > Semantic Segmentation | LIP val | UniHCP (finetune) | https://arxiv.org/abs/2303.02936v4 | mIoU | 63.86% |
10-shot image generation > Semantic Segmentation | LIP val | SOLIDER | https://arxiv.org/abs/2303.17602v1 | mIoU | 60.50% |
10-shot image generation > Semantic Segmentation | LIP val | HRNetV2 + OCR + RMI (PaddleClas pretrained) | https://arxiv.org/abs/1909.11065v6 | mIoU | 58.2% |
10-shot image generation > Semantic Segmentation | LIP val | OCR (HRNetV2-W48) | https://arxiv.org/abs/1909.11065v6 | mIoU | 56.65% |
10-shot image generation > Semantic Segmentation | LIP val | HRNetV2 (HRNetV2-W48) | http://arxiv.org/abs/1904.04514v1 | mIoU | 55.90% |
10-shot image generation > Semantic Segmentation | LIP val | OCR (ResNet-101) | https://arxiv.org/abs/1909.11065v6 | mIoU | 55.6% |
10-shot image generation > Semantic Segmentation | LIP val | CE2P (ResNet-101) | http://arxiv.org/abs/1809.05996v3 | mIoU | 53.10% |
10-shot image generation > Semantic Segmentation | LIP val | JPPNet (ResNet-101) | http://arxiv.org/abs/1804.01984v1 | mIoU | 51.37% |
10-shot image generation > Semantic Segmentation | LIP val | MuLA (ResNet-101) | http://openaccess.thecvf.com/content_ECCV_2018/html/Xuecheng_Nie_Mutual_Learning_to_ECCV_2018_paper.html | mIoU | 49.30% |
10-shot image generation > Semantic Segmentation | LIP val | MMAN (ResNet-101) | http://arxiv.org/abs/1807.08260v2 | mIoU | 46.81% |
10-shot image generation > Semantic Segmentation | LIP val | Attention+SSL (ResNet-101) | http://arxiv.org/abs/1703.05446v2 | mIoU | 44.73% |
10-shot image generation > Semantic Segmentation | COCO-Stuff-27 | DiffSeg (512) | https://arxiv.org/abs/2308.12469v3 | mIoU | 43.6 |
10-shot image generation > Semantic Segmentation | COCO-Stuff-27 | DiffSeg (512) | https://arxiv.org/abs/2308.12469v3 | Pixel Accuracy | 72.5 |
10-shot image generation > Semantic Segmentation | DensePASS | Trans4PASS+ (multi-scale) | https://arxiv.org/abs/2207.11860v5 | mIoU | 57.23% |
10-shot image generation > Semantic Segmentation | DensePASS | Trans4PASS+ (single-scale) | https://arxiv.org/abs/2207.11860v5 | mIoU | 56.45% |
10-shot image generation > Semantic Segmentation | DensePASS | Trans4PASS (multi-scale) | https://arxiv.org/abs/2203.01452v2 | mIoU | 56.38% |
10-shot image generation > Semantic Segmentation | DensePASS | Trans4PASS (single-scale) | https://arxiv.org/abs/2203.01452v2 | mIoU | 55.25% |
10-shot image generation > Semantic Segmentation | DensePASS | DAFormer | https://arxiv.org/abs/2111.14887v2 | mIoU | 54.67% |
10-shot image generation > Semantic Segmentation | DensePASS | PCS | https://arxiv.org/abs/2103.16765v1 | mIoU | 53.83% |
10-shot image generation > Semantic Segmentation | DensePASS | P2PDA (Cityscapes+WildDash) | https://arxiv.org/abs/2110.11062v1 | mIoU | 48.52% |
10-shot image generation > Semantic Segmentation | DensePASS | SIM | https://arxiv.org/abs/2003.08040v3 | mIoU | 44.58% |
10-shot image generation > Semantic Segmentation | DensePASS | PoolFormer (MiT-B1) | https://arxiv.org/abs/2111.11418v3 | mIoU | 43.18% |
10-shot image generation > Semantic Segmentation | DensePASS | ECANet | https://arxiv.org/abs/2103.05687v1 | mIoU | 43.02% |
10-shot image generation > Semantic Segmentation | DensePASS | FAN (MiT-B1) | https://arxiv.org/abs/2204.12451v4 | mIoU | 42.54% |
10-shot image generation > Semantic Segmentation | DensePASS | SegFormer (MiT-B2) | https://arxiv.org/abs/2105.15203v3 | mIoU | 42.4% |
10-shot image generation > Semantic Segmentation | DensePASS | ASMLP (MiT-B1) | https://arxiv.org/abs/2107.08391v2 | mIoU | 42.05% |
10-shot image generation > Semantic Segmentation | DensePASS | P2PDA (Cityscapes) | https://arxiv.org/abs/2110.11062v1 | mIoU | 41.99% |
10-shot image generation > Semantic Segmentation | DensePASS | CycleMLP (MiT-B1) | https://arxiv.org/abs/2107.10224v4 | mIoU | 40.16% |
10-shot image generation > Semantic Segmentation | DensePASS | SegFormer (MiT-B1) | https://arxiv.org/abs/2105.15203v3 | mIoU | 38.5% |
10-shot image generation > Semantic Segmentation | DensePASS | DPT (MiT-B1) | https://arxiv.org/abs/2107.14467v1 | mIoU | 36.50% |
10-shot image generation > Semantic Segmentation | DensePASS | SETR (PUP, Transformer-L) | https://arxiv.org/abs/2012.15840v3 | mIoU | 35.7% |
10-shot image generation > Semantic Segmentation | DensePASS | SETR (MLA, Transformer-L) | https://arxiv.org/abs/2012.15840v3 | mIoU | 35.6% |
10-shot image generation > Semantic Segmentation | DensePASS | Seamless (Mapillary) | https://arxiv.org/abs/1905.01220v1 | mIoU | 34.14% |
10-shot image generation > Semantic Segmentation | DensePASS | DeepLabV3+ (ResNet-101) | http://arxiv.org/abs/1802.02611v3 | mIoU | 32.5% |
10-shot image generation > Semantic Segmentation | DensePASS | DNL (ResNet-101) | https://arxiv.org/abs/2006.06668v2 | mIoU | 32.1% |
10-shot image generation > Semantic Segmentation | DensePASS | SwiftNet (Merge3) | https://arxiv.org/abs/2008.08974v2 | mIoU | 32.04% |
10-shot image generation > Semantic Segmentation | DensePASS | CRST | https://arxiv.org/abs/1908.09822v3 | mIoU | 31.67% |
10-shot image generation > Semantic Segmentation | DensePASS | CLAN | http://arxiv.org/abs/1809.09478v3 | mIoU | 31.46% |
10-shot image generation > Semantic Segmentation | DensePASS | PVT (Tiny, FPN) | https://arxiv.org/abs/2102.12122v2 | mIoU | 31.20% |
10-shot image generation > Semantic Segmentation | DensePASS | USSS (Mapillary) | https://arxiv.org/abs/1811.10323v3 | mIoU | 30.87% |
10-shot image generation > Semantic Segmentation | DensePASS | PSPNet (ResNet-50) | http://arxiv.org/abs/1612.01105v2 | mIoU | 29.5% |
10-shot image generation > Semantic Segmentation | DensePASS | Semantic-FPN (ResNet-101) | http://arxiv.org/abs/1901.02446v2 | mIoU | 28.8% |
10-shot image generation > Semantic Segmentation | DensePASS | DANet (ResNet-101) | http://arxiv.org/abs/1809.02983v4 | mIoU | 28.5% |
10-shot image generation > Semantic Segmentation | DensePASS | USSS (IDD) | https://arxiv.org/abs/1811.10323v3 | mIoU | 26.98% |
10-shot image generation > Semantic Segmentation | DensePASS | FANet (Resnet-34) | https://arxiv.org/abs/2007.03815v2 | mIoU | 26.9% |
10-shot image generation > Semantic Segmentation | DensePASS | SwiftNet (Cityscapes) | http://arxiv.org/abs/1903.08469v2 | mIoU | 25.67% |
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