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 ⌀ |
|---|---|---|---|---|---|
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | Video K-Net (Swin-B) | https://arxiv.org/abs/2204.04656v2 | VPQ (thing) | 49.8 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | Video K-Net (Swin-B) | https://arxiv.org/abs/2204.04656v2 | VPQ (stuff) | 71.8 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ | 58.9 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 43.7 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 69.9 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ | 58.0 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 42.9 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 69.0 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ | 57.3 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ (thing) | 44.7 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet-SiamTrack | https://arxiv.org/abs/2106.09453v1 | VPQ (stuff) | 66.4 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ | 57.0 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ (thing) | 44.7 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | VPSNet | https://arxiv.org/abs/2006.11339v1 | VPQ (stuff) | 66.0 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ | 53.3 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ (thing) | 35.9 |
Panoptic Segmentation > Video Panoptic Segmentation | Cityscapes-VPS | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ (stuff) | 66.0 |
Panoptic Segmentation > Video Panoptic Segmentation | 4D-OR | MM-OR-VPQ4 | https://arxiv.org/abs/2503.02579v1 | VPQ | 69.8 |
Panoptic Segmentation > Video Panoptic Segmentation | 4D-OR | MM-OR-VPQ8 | https://arxiv.org/abs/2503.02579v1 | VPQ | 69.2 |
Panoptic Segmentation > Video Panoptic Segmentation | MM-OR | MM-OR-VPQ4 | https://arxiv.org/abs/2503.02579v1 | VPQ | 67.0 |
Panoptic Segmentation > Video Panoptic Segmentation | MM-OR | MM-OR-VPQ8 | https://arxiv.org/abs/2503.02579v1 | VPQ | 66.4 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | CAVIS(VIT-L) | https://arxiv.org/abs/2407.03010v1 | VPQ | 58.5 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | CAVIS(VIT-L) | https://arxiv.org/abs/2407.03010v1 | STQ | 56.1 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS++(VIT-L) | https://arxiv.org/abs/2312.13305v1 | VPQ | 58.0 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS++(VIT-L) | https://arxiv.org/abs/2312.13305v1 | STQ | 56.0 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ConvNeXtV2-L) | https://arxiv.org/abs/2311.18537v2 | VPQ | 58.0 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS(Swin-L) | https://arxiv.org/abs/2306.03413v3 | VPQ | 57.6 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DVIS(Swin-L) | https://arxiv.org/abs/2306.03413v3 | STQ | 55.3 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ConvNeXt-L) | https://arxiv.org/abs/2311.18537v2 | VPQ | 57.1 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DEVA (Mask2Former - SwinB) | https://arxiv.org/abs/2309.03903v1 | VPQ | 55.0 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | DEVA (Mask2Former - SwinB) | https://arxiv.org/abs/2309.03903v1 | STQ | 52.2 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | VPQ | 50.4 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | STQ | 49.4 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | UniVS(Swin-L) | https://arxiv.org/abs/2402.18115v2 | VPQ | 49.3 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | UniVS(Swin-L) | https://arxiv.org/abs/2402.18115v2 | STQ | 58.2 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | VPQ | 48.0 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | STQ | 52.9 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | MaXTron (ResNet-50) | https://arxiv.org/abs/2311.18537v2 | VPQ | 46.7 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | VPQ | 35.8 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | STQ | 45.3 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | VPQ | 33.5 |
Panoptic Segmentation > Video Panoptic Segmentation | VIPSeg | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | STQ | 43.1 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | STQ | 74.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | AQ | 73.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Video K-Net (Swin-L) | https://arxiv.org/abs/2204.04656v2 | SQ | 75.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | STQ | 73.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | AQ | 72.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-L) | https://arxiv.org/abs/2301.02657v2 | SQ | 72.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | STQ | 70.6 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | AQ | 71.2 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (Swin-T) | https://arxiv.org/abs/2301.02657v2 | SQ | 69.9 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | STQ | 69.6 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | AQ | 70.3 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | TarViS (ResNet-50) | https://arxiv.org/abs/2301.02657v2 | SQ | 68.8 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | STQ | 72.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | AQ | 69.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Tube-Link(Swin-base) | https://arxiv.org/abs/2303.12782v3 | SQ | 74.0 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | STQ | 59.1 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | AQ | 56.4 |
Panoptic Segmentation > Video Panoptic Segmentation | KITTI-STEP | Unified Perception | https://arxiv.org/abs/2303.01991v2 | SQ | 61.9 |
Panoptic Segmentation > Uncertainty-Aware Panoptic Segmentation | MUSES: MUlti-SEnsor Semantic perception dataset | Mask2Former (Swin-T) | https://arxiv.org/abs/2401.12761v4 | AUPQ | 44.3 |
3D Object Super-Resolution > Super-Resolution | hradis et al dataset | super-resolution | https://arxiv.org/abs/2201.05865v1 | Average PSNR | 20.406 |
3D Object Super-Resolution > Super-Resolution | hradis et al dataset | super-resolution | https://arxiv.org/abs/2201.05865v1 | SSIM | 0.877 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Chikusei Dataset | DIP-HyperKite (ours) | https://arxiv.org/abs/2107.02630v1 | PSNR | 43.53 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | DRLN+ | https://arxiv.org/abs/1906.12021v2 | PSNR | 23.24 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | DRLN+ | https://arxiv.org/abs/1906.12021v2 | SSIM | 0.6523 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | DBPN-RES-MR64-3 | https://arxiv.org/abs/1904.05677v2 | PSNR | 23.2 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | DBPN-RES-MR64-3 | https://arxiv.org/abs/1904.05677v2 | SSIM | 0.652 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | HAN+ | https://arxiv.org/abs/2008.08767v1 | PSNR | 23.20 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | HAN+ | https://arxiv.org/abs/2008.08767v1 | SSIM | 0.6518 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | HBPN | https://arxiv.org/abs/1906.06874v2 | PSNR | 23.04 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | HBPN | https://arxiv.org/abs/1906.06874v2 | SSIM | 0.647 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | PSNR | 23.04 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Urban100 - 8x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | SSIM | 0.641 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Set5 - 6x upscaling | HyperRes | https://arxiv.org/abs/2206.05970v3 | PSNR | 24.92 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Set5 - 6x upscaling | HyperRes | https://arxiv.org/abs/2206.05970v3 | SSIM | 0.71 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD200 - 2x upscaling | CSRCNN | https://arxiv.org/abs/2008.10329v2 | PSNR | 32.92 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD200 - 2x upscaling | CSRCNN | https://arxiv.org/abs/2008.10329v2 | SSIM | 0.9122 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Middlebury - 4x upscaling | PASSRnet | http://arxiv.org/abs/1903.05784v3 | PSNR | 28.63 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Manga109 - 16x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | PSNR | 21.25 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | Manga109 - 16x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | SSIM | 0.673 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR-V2 | https://arxiv.org/abs/2409.10582v3 | PSNR | 33.12 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR-V2 | https://arxiv.org/abs/2409.10582v3 | SSIM | 0.9326 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | https://arxiv.org/abs/2307.00430v1 | PSNR | 33.08 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | https://arxiv.org/abs/2307.00430v1 | SSIM | 0.9322 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | DRCT-L | https://arxiv.org/abs/2404.00722v5 | PSNR | 32.90 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | DRCT-L | https://arxiv.org/abs/2404.00722v5 | SSIM | 0.9078 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HMA† | https://arxiv.org/abs/2405.05001v1 | PSNR | 32.79 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HMA† | https://arxiv.org/abs/2405.05001v1 | SSIM | 0.9071 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | Hi-IR-L | https://arxiv.org/abs/2411.18588v1 | PSNR | 32.77 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | Hi-IR-L | https://arxiv.org/abs/2411.18588v1 | SSIM | 0.9092 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | DRCT | https://arxiv.org/abs/2404.00722v5 | PSNR | 32.75 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | DRCT | https://arxiv.org/abs/2404.00722v5 | SSIM | 0.9071 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HAT-L | https://arxiv.org/abs/2205.04437v3 | PSNR | 32.74 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HAT-L | https://arxiv.org/abs/2205.04437v3 | SSIM | 0.9066 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HAT_FIR | https://arxiv.org/abs/2208.11247v3 | PSNR | 32.71 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HAT | https://arxiv.org/abs/2205.04437v3 | PSNR | 32.69 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | HAT | https://arxiv.org/abs/2205.04437v3 | SSIM | 0.9060 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | CPAT+ | https://arxiv.org/abs/2407.16232v2 | PSNR | 32.66 |
3D Object Super-Resolution > Super-Resolution > Image Super-Resolution | BSD100 - 2x upscaling | CPAT+ | https://arxiv.org/abs/2407.16232v2 | SSIM | 0.9058 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.