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 > Panoptic Segmentation | COCO minival | Axial-DeepLab-L(multi-scale) | https://arxiv.org/abs/2003.07853v2 | PQth | 48.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Axial-DeepLab-L(multi-scale) | https://arxiv.org/abs/2003.07853v2 | PQst | 36.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | SQ | 83.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | RQ | 61.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | PQth | 58.5 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | SQth | 84.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | RQth | 68.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | SQst | 81.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation | COCO minival | Panoptic FCN* (Swin-L, single-scale) | https://arxiv.org/abs/2012.00720v2 | RQst | 51.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | https://arxiv.org/abs/2012.05258v1 | VPQ | 63.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | https://arxiv.org/abs/2012.05258v1 | VPQ (thing) | 49.5 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | https://arxiv.org/abs/2012.05258v1 | VPQ (stuff) | 73.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | PolyphonicFormer | https://arxiv.org/abs/2112.02582v4 | VPQ | 62.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | PolyphonicFormer | https://arxiv.org/abs/2112.02582v4 | VPQ (thing) | - |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | PolyphonicFormer | https://arxiv.org/abs/2112.02582v4 | VPQ (stuff) | - |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | Video K-Net (Swin-B) | https://arxiv.org/abs/2204.04656v2 | VPQ | 62.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | Video K-Net (Swin-B) | https://arxiv.org/abs/2204.04656v2 | VPQ (thing) | 49.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | Video K-Net (Swin-B) | https://arxiv.org/abs/2204.04656v2 | VPQ (stuff) | 71.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ | 58.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 43.7 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 69.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ | 58.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 42.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 69.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ | 57.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ (thing) | 44.7 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ (stuff) | 66.4 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ | 57.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ (thing) | 44.7 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ (stuff) | 66.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ | 53.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 35.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 66.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | 4D-OR | MM-OR-VPQ4 | https://arxiv.org/abs/2503.02579v1 | VPQ | 69.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | 4D-OR | MM-OR-VPQ8 | https://arxiv.org/abs/2503.02579v1 | VPQ | 69.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | MM-OR | MM-OR-VPQ4 | https://arxiv.org/abs/2503.02579v1 | VPQ | 67.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | MM-OR | MM-OR-VPQ8 | https://arxiv.org/abs/2503.02579v1 | VPQ | 66.4 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | CAVIS(VIT-L) | https://arxiv.org/abs/2407.03010v1 | VPQ | 58.5 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | CAVIS(VIT-L) | https://arxiv.org/abs/2407.03010v1 | STQ | 56.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS++(VIT-L) | https://arxiv.org/abs/2312.13305v1 | VPQ | 58.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS++(VIT-L) | https://arxiv.org/abs/2312.13305v1 | STQ | 56.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ConvNeXtV2-L) | https://arxiv.org/abs/2311.18537v2 | VPQ | 58.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS(Swin-L) | https://arxiv.org/abs/2306.03413v3 | VPQ | 57.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS(Swin-L) | https://arxiv.org/abs/2306.03413v3 | STQ | 55.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ConvNeXt-L) | https://arxiv.org/abs/2311.18537v2 | VPQ | 57.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DEVA (Mask2Former - SwinB) | https://arxiv.org/abs/2309.03903v1 | VPQ | 55.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DEVA (Mask2Former - SwinB) | https://arxiv.org/abs/2309.03903v1 | STQ | 52.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | VPQ | 50.4 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | STQ | 49.4 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | UniVS(Swin-L) | https://arxiv.org/abs/2402.18115v2 | VPQ | 49.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | UniVS(Swin-L) | https://arxiv.org/abs/2402.18115v2 | STQ | 58.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ | 48.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | STQ | 52.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ResNet-50) | https://arxiv.org/abs/2311.18537v2 | VPQ | 46.7 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ | 35.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | STQ | 45.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ | 33.5 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | STQ | 43.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | STQ | 74.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | AQ | 73.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | SQ | 75.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | STQ | 73.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | AQ | 72.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | SQ | 72.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | STQ | 70.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | AQ | 71.2 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | SQ | 69.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | STQ | 69.6 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | AQ | 70.3 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | SQ | 68.8 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | STQ | 72.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | AQ | 69.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | SQ | 74.0 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | STQ | 59.1 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | AQ | 56.4 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | SQ | 61.9 |
10-shot image generation > Semantic Segmentation > Panoptic Segmentation > Uncertainty-Aware Panoptic Segmentation | MUSES: MUlti-SEnsor Semantic perception dataset | Mask2Former (Swin-T) | https://arxiv.org/abs/2401.12761v4 | AUPQ | 44.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | DITR | https://arxiv.org/abs/2503.18944v1 | val mIoU | 41.2 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | DITR | https://arxiv.org/abs/2503.18944v1 | test mIoU | 44.9 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | ODIN | https://arxiv.org/abs/2401.02416v3 | val mIoU | 40.5 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | ODIN | https://arxiv.org/abs/2401.02416v3 | test mIoU | 36.8 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PTv3 ArKitLabelmaker | https://arxiv.org/abs/2410.13924v1 | val mIoU | 40.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PTv3 ArKitLabelmaker | https://arxiv.org/abs/2410.13924v1 | test mIoU | 41.4 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | BFANet | https://arxiv.org/abs/2503.12539v1 | val mIoU | 37.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | BFANet | https://arxiv.org/abs/2503.12539v1 | test mIoU | 36.0 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | Sonata + PTv3 | https://arxiv.org/abs/2503.16429v1 | val mIoU | 36.8 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | Pamba | https://arxiv.org/abs/2406.17442v3 | val mIoU | 36.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | Pamba | https://arxiv.org/abs/2406.17442v3 | test mIoU | 37.1 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PTv3 + PPT | https://arxiv.org/abs/2312.10035v2 | val mIoU | 36.0 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PTv3 + PPT | https://arxiv.org/abs/2312.10035v2 | test mIoU | 39.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | OA-CNNs | https://arxiv.org/abs/2403.14418v1 | val mIoU | 33.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | OA-CNNs | https://arxiv.org/abs/2403.14418v1 | test mIoU | 32.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | LSK3DNet | https://arxiv.org/abs/2403.15173v1 | val mIoU | 33.1 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | OctFormer | https://arxiv.org/abs/2305.03045v2 | val mIoU | 32.6 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | OctFormer | https://arxiv.org/abs/2305.03045v2 | test mIoU | 32.5 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PonderV2 + SparseUNet | https://arxiv.org/abs/2310.08586v3 | val mIoU | 32.3 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PonderV2 + SparseUNet | https://arxiv.org/abs/2310.08586v3 | test mIoU | 34.6 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PPT+SparseUNet | https://arxiv.org/abs/2308.09718v2 | val mIoU | 31.9 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | PPT+SparseUNet | https://arxiv.org/abs/2308.09718v2 | test mIoU | 33.2 |
10-shot image generation > Semantic Segmentation > 3D Semantic Segmentation | ScanNet200 | OneFormer3D | https://arxiv.org/abs/2311.14405v1 | val mIoU | 30.1 |
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