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 ⌀ |
|---|---|---|---|---|---|
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | MonoDETR | https://arxiv.org/abs/2203.13310v5 | AP25 | 27.13 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | GrooMeD-NMS | https://arxiv.org/abs/2103.17202v1 | AP50 | 0.17 |
16k > Object Detection > 3D Object Detection From Monocular Images | KITTI-360 | GrooMeD-NMS | https://arxiv.org/abs/2103.17202v1 | AP25 | 16.12 |
16k > Object Detection > One-Shot Object Detection | PASCAL VOC 2012 val | QDTrack | https://arxiv.org/abs/2006.06664v4 | MAP | 22.1 |
16k > Object Detection > One-Shot Object Detection | COCO (Common Objects in Context) | OWL-ViT (R50+H/32) | https://arxiv.org/abs/2205.06230v2 | AP 0.5 | 41.8 |
16k > Object Detection > One-Shot Object Detection | COCO (Common Objects in Context) | DE-ViT | https://arxiv.org/abs/2309.12969v4 | AP 0.5 | 28.4 |
16k > Object Detection > One-Shot Object Detection | COCO (Common Objects in Context) | One-Shot Object Detection | https://arxiv.org/abs/1911.12529v1 | AP 0.5 | 22.0 |
16k > Object Detection > One-Shot Object Detection | COCO (Common Objects in Context) | Siamese Mask R-CNN | https://arxiv.org/abs/1811.11507v2 | AP 0.5 | 16.3 |
16k > Object Detection > Surgical tool detection | HeiChole Benchmark | MoCo V2 Surg SSL - FCN head | https://arxiv.org/abs/2207.00449v3 | mAP | 66.9 |
16k > Object Detection > Surgical tool detection | Cholec80 | MoCo V2 Surg SSL - FCN head | https://arxiv.org/abs/2207.00449v3 | mAP | 93.5 |
16k > Object Detection > Surgical tool detection | Cholec80 | ConvLSTM tracker | http://arxiv.org/abs/1812.01366v2 | mAP | 92.9 |
16k > Object Detection > Surgical tool detection | Cholec80 | MTRCNet-CL | https://arxiv.org/abs/1907.06099v1 | mAP | 89.1 |
16k > Object Detection > Surgical tool detection | Cholec80 | FCN | http://arxiv.org/abs/1806.05573v2 | mAP | 87.4 |
16k > Object Detection > Surgical tool detection | Cholec80 | EndoNet | http://arxiv.org/abs/1602.03012v2 | mAP | 81.0 |
16k > Object Detection > Surgical tool detection | Cholec80 | ToolNet | http://arxiv.org/abs/1602.03012v2 | mAP | 80.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | MM-Grounding-DINO | https://arxiv.org/abs/2401.02361v2 | Intra-scenario FULL mAP | 22.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | MM-Grounding-DINO | https://arxiv.org/abs/2401.02361v2 | Intra-scenario PRES mAP | 21.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | MM-Grounding-DINO | https://arxiv.org/abs/2401.02361v2 | Intra-scenario ABS mAP | 26.0 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | FIBER-B | https://arxiv.org/abs/2206.07643v2 | Intra-scenario FULL mAP | 22.7 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | FIBER-B | https://arxiv.org/abs/2206.07643v2 | Intra-scenario PRES mAP | 21.5 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | FIBER-B | https://arxiv.org/abs/2206.07643v2 | Intra-scenario ABS mAP | 26.0 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OFA-DOD-base | https://arxiv.org/abs/2307.12813v2 | Intra-scenario FULL mAP | 21.6 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OFA-DOD-base | https://arxiv.org/abs/2307.12813v2 | Intra-scenario PRES mAP | 23.7 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OFA-DOD-base | https://arxiv.org/abs/2307.12813v2 | Intra-scenario ABS mAP | 15.4 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | GLIP-T | https://arxiv.org/abs/2112.03857v2 | Intra-scenario FULL mAP | 19.1 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | GLIP-T | https://arxiv.org/abs/2112.03857v2 | Intra-scenario PRES mAP | 18.3 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | GLIP-T | https://arxiv.org/abs/2112.03857v2 | Intra-scenario ABS mAP | 21.5 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | UNINEXT-large | https://arxiv.org/abs/2303.06674v2 | Intra-scenario FULL mAP | 17.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | UNINEXT-large | https://arxiv.org/abs/2303.06674v2 | Intra-scenario PRES mAP | 18.6 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | UNINEXT-large | https://arxiv.org/abs/2303.06674v2 | Intra-scenario ABS mAP | 15.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | SPHINX-7B | https://arxiv.org/abs/2311.07575v1 | Intra-scenario FULL mAP | 10.6 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | SPHINX-7B | https://arxiv.org/abs/2311.07575v1 | Intra-scenario PRES mAP | 11.4 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | SPHINX-7B | https://arxiv.org/abs/2311.07575v1 | Intra-scenario ABS mAP | 7.9 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OWL-ViT-base | https://arxiv.org/abs/2205.06230v2 | Intra-scenario FULL mAP | 8.6 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OWL-ViT-base | https://arxiv.org/abs/2205.06230v2 | Intra-scenario PRES mAP | 8.5 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | OWL-ViT-base | https://arxiv.org/abs/2205.06230v2 | Intra-scenario ABS mAP | 8.8 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | CORA-R50 | https://arxiv.org/abs/2303.13076v1 | Intra-scenario FULL mAP | 6.2 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | CORA-R50 | https://arxiv.org/abs/2303.13076v1 | Intra-scenario PRES mAP | 6.7 |
16k > Object Detection > Described Object Detection | Description Detection Dataset | CORA-R50 | https://arxiv.org/abs/2303.13076v1 | Intra-scenario ABS mAP | 5.0 |
16k > Object Detection > Body Detection | Manga109 | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | Average Precision | 87.98 |
16k > Object Detection > Body Detection | DCM | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | Average Precision | 86.14 |
16k > Object Detection > Body Detection | DCM | DASS-Detector (YOLOX Tiny) | https://arxiv.org/abs/2211.10641v2 | Average Precision | 87.06 |
16k > Object Detection > Body Detection | Clipart1k | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | MAP | 83.59 |
16k > Object Detection > Body Detection | Watercolor2k | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | MAP | 89.81 |
16k > Object Detection > Body Detection | Comic2k | DASS-Detector (YOLOX XL) | https://arxiv.org/abs/2211.10641v2 | MAP | 73.65 |
16k > Object Detection > Pupil Detection | INI-30 | CNN | https://arxiv.org/abs/2312.00425v2 | Euclidean Distance | 0.5 |
16k > Object Detection > Pupil Detection | INI-30 | TinyissimoV8 | https://arxiv.org/abs/2312.00425v2 | Euclidean Distance | 1.75 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | YONOD + CPPM (RGB + Depth) | https://arxiv.org/abs/2207.01071v2 | AP 0.5 | 58.1 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | Frustum Pointnet (RGB) | http://arxiv.org/abs/1711.08488v2 | AP 0.5 | 56.8 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | simCrossTrans with Swin Transformer (Point Cloud only) | https://arxiv.org/abs/2203.10456v1 | AP 0.5 | 55.8 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | 2D - Driven (RGB) | http://openaccess.thecvf.com/content_iccv_2017/html/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.html | AP 0.5 | 49.7 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | Frustum VoxNet (RGB) | https://arxiv.org/abs/1910.05483v3 | AP 0.5 | 47.9 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | RGB-D RCNN (RGB + Depth) | http://arxiv.org/abs/1407.5736v1 | AP 0.5 | 44.2 |
16k > Object Detection > Object Detection In Indoor Scenes | SUN RGB-D | Frustum VoxNet (Depth only) | https://arxiv.org/abs/1910.05483v3 | AP 0.5 | 42.8 |
16k > Object Detection > Weakly Supervised 3D Detection | KITTI-360 | VSRD-MonoDETR | https://arxiv.org/abs/2404.00149v1 | mAP@0.3 | 58.40 |
16k > Object Detection > Weakly Supervised 3D Detection | KITTI-360 | Auto-Labels | https://arxiv.org/abs/1911.11288v2 | mAP@0.3 | 48.16 |
16k > Object Detection > Weakly Supervised 3D Detection | KITTI-360 | WeakM3D | https://arxiv.org/abs/2203.08332v1 | mAP@0.3 | 29.89 |
16k > Object Detection > Object Skeleton Detection | SK-LARGE | DeepFlux | http://arxiv.org/abs/1811.12608v1 | F-Measure | 0.732 |
16k > Object Detection > Object Skeleton Detection | SK-LARGE | Hi-Fi | http://arxiv.org/abs/1801.01849v4 | F-Measure | 0.724 |
16k > Object Detection > Semantic Part Detection | PASCAL Part 2010 - Animals | Attention-based Joint Detection of Object and Semantic Part | https://arxiv.org/abs/2007.02419v1 | mAP@0.5 | 52.0 |
16k > Object Detection > Malaria Falciparum Detection | M5-Malaria Dataset | YOLO Para | https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor | AP | 71.0 |
16k > Object Detection > Malaria Falciparum Detection | MP-IDB | YOLO Para | https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor | AP | 86.5 |
16k > Object Detection > Malaria Vivax Detection | MP-IDB | YOLO Para | https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor | AP | 88.3 |
16k > Object Detection > Malaria Malariae Detection | MP-IDB | YOLO Para | https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor | AP | 94.9 |
16k > Object Detection > Malaria Ovale Detection | MP-IDB | YOLO Para | https://www.sciencedirect.com/science/article/pii/S001048252500054X?dgcid=coauthor | AP | 95.1 |
16k > Image Super-Resolution | Chikusei Dataset | DIP-HyperKite (ours) | https://arxiv.org/abs/2107.02630v1 | PSNR | 43.53 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | DRLN+ | https://arxiv.org/abs/1906.12021v2 | PSNR | 23.24 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | DRLN+ | https://arxiv.org/abs/1906.12021v2 | SSIM | 0.6523 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | DBPN-RES-MR64-3 | https://arxiv.org/abs/1904.05677v2 | PSNR | 23.2 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | DBPN-RES-MR64-3 | https://arxiv.org/abs/1904.05677v2 | SSIM | 0.652 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | HAN+ | https://arxiv.org/abs/2008.08767v1 | PSNR | 23.20 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | HAN+ | https://arxiv.org/abs/2008.08767v1 | SSIM | 0.6518 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | HBPN | https://arxiv.org/abs/1906.06874v2 | PSNR | 23.04 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | HBPN | https://arxiv.org/abs/1906.06874v2 | SSIM | 0.647 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | PSNR | 23.04 |
16k > Image Super-Resolution | Urban100 - 8x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | SSIM | 0.641 |
16k > Image Super-Resolution | Set5 - 6x upscaling | HyperRes | https://arxiv.org/abs/2206.05970v3 | PSNR | 24.92 |
16k > Image Super-Resolution | Set5 - 6x upscaling | HyperRes | https://arxiv.org/abs/2206.05970v3 | SSIM | 0.71 |
16k > Image Super-Resolution | BSD200 - 2x upscaling | CSRCNN | https://arxiv.org/abs/2008.10329v2 | PSNR | 32.92 |
16k > Image Super-Resolution | BSD200 - 2x upscaling | CSRCNN | https://arxiv.org/abs/2008.10329v2 | SSIM | 0.9122 |
16k > Image Super-Resolution | Middlebury - 4x upscaling | PASSRnet | http://arxiv.org/abs/1903.05784v3 | PSNR | 28.63 |
16k > Image Super-Resolution | Manga109 - 16x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | PSNR | 21.25 |
16k > Image Super-Resolution | Manga109 - 16x upscaling | ABPN | https://arxiv.org/abs/1910.04476v1 | SSIM | 0.673 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR-V2 | https://arxiv.org/abs/2409.10582v3 | PSNR | 33.12 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR-V2 | https://arxiv.org/abs/2409.10582v3 | SSIM | 0.9326 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | https://arxiv.org/abs/2307.00430v1 | PSNR | 33.08 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | https://arxiv.org/abs/2307.00430v1 | SSIM | 0.9322 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | DRCT-L | https://arxiv.org/abs/2404.00722v5 | PSNR | 32.90 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | DRCT-L | https://arxiv.org/abs/2404.00722v5 | SSIM | 0.9078 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HMA† | https://arxiv.org/abs/2405.05001v1 | PSNR | 32.79 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HMA† | https://arxiv.org/abs/2405.05001v1 | SSIM | 0.9071 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | Hi-IR-L | https://arxiv.org/abs/2411.18588v1 | PSNR | 32.77 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | Hi-IR-L | https://arxiv.org/abs/2411.18588v1 | SSIM | 0.9092 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | DRCT | https://arxiv.org/abs/2404.00722v5 | PSNR | 32.75 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | DRCT | https://arxiv.org/abs/2404.00722v5 | SSIM | 0.9071 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HAT-L | https://arxiv.org/abs/2205.04437v3 | PSNR | 32.74 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HAT-L | https://arxiv.org/abs/2205.04437v3 | SSIM | 0.9066 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HAT_FIR | https://arxiv.org/abs/2208.11247v3 | PSNR | 32.71 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HAT | https://arxiv.org/abs/2205.04437v3 | PSNR | 32.69 |
16k > Image Super-Resolution | BSD100 - 2x upscaling | HAT | https://arxiv.org/abs/2205.04437v3 | SSIM | 0.9060 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.