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 ⌀ |
|---|---|---|---|---|---|
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | RawGAT-ST | https://arxiv.org/abs/2107.12710v2 | 21DF EER | 23.26 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | AASIST | https://arxiv.org/abs/2110.01200v1 | 21LA EER | 11.46 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | AASIST | https://arxiv.org/abs/2110.01200v1 | 21DF EER | 21.07 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | RawNet-2 | https://arxiv.org/abs/2011.01108v3 | 21LA EER | 40.07 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | RawNet-2 | https://arxiv.org/abs/2011.01108v3 | 21DF EER | 40.06 |
3D Shape Reconstruction from Videos > DeepFake Detection > Multimodal Forgery Detection | FakeAVCeleb | Ensemble AudioVisual Model | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-6/1570840386.pdf | Accuracy (%) | 0.89 |
16k | ConceptNet | Suprime2 | https://arxiv.org/abs/2104.11037v1 | 1'" | 1 |
16k > Object Detection | CISOL - Track B - TSR-only | YOLO v8.1m | https://arxiv.org/abs/2501.15469v1 | mAP@0.5:0.95:0.05 | 61.39 |
16k > Object Detection | Extended TACO-7 | EfficientDet-D2 | https://arxiv.org/abs/2105.06808v1 | mAP50 | 16.2 |
16k > Object Detection | CISOL - Track A - TD-TSR | YOLO v8.1m | https://arxiv.org/abs/2501.15469v1 | mAP@0.5:0.95:0.05 | 67.22 |
16k > Object Detection | TexBiG 2023 test | DetectoRS + LAEM | https://arxiv.org/abs/2309.09742v1 | mAP@0.5:0.95:0.05 | 49.89 |
16k > Object Detection | LVIS v1.0 | ScaleDet | https://arxiv.org/abs/2306.04849v1 | box AP | 50.7 |
16k > Object Detection | CrowdHuman (full body) | InternImage-H | https://arxiv.org/abs/2211.05778v4 | AP | 97.2 |
16k > Object Detection | CrowdHuman (full body) | MMPedestron | https://arxiv.org/abs/2407.10125v1 | AP | 97.1 |
16k > Object Detection | CrowdHuman (full body) | MMPedestron | https://arxiv.org/abs/2407.10125v1 | mMR | 30.8 |
16k > Object Detection | CrowdHuman (full body) | Progressive DETR | https://arxiv.org/abs/2203.07669v3 | AP | 94.1 |
16k > Object Detection | CrowdHuman (full body) | Progressive DETR | https://arxiv.org/abs/2203.07669v3 | mMR | 37.7 |
16k > Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | https://arxiv.org/abs/2303.12776v2 | AP | 93.8 |
16k > Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | https://arxiv.org/abs/2303.12776v2 | mMR | 39.7 |
16k > Object Detection | CrowdHuman (full body) | DDQ DETR (R50) | https://arxiv.org/abs/2303.12776v2 | Recall | 98.7 |
16k > Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | https://arxiv.org/abs/2303.12776v2 | AP | 93.5 |
16k > Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | https://arxiv.org/abs/2303.12776v2 | mMR | 40.4 |
16k > Object Detection | CrowdHuman (full body) | DDQ R-CNN (R50) | https://arxiv.org/abs/2303.12776v2 | Recall | 98.6 |
16k > Object Detection | CrowdHuman (full body) | Hulk(Finetune, ViT-L) | https://arxiv.org/abs/2312.01697v4 | AP | 93 |
16k > Object Detection | CrowdHuman (full body) | Hulk(Finetune, ViT-L) | https://arxiv.org/abs/2312.01697v4 | mMR | 36.5 |
16k > Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | https://arxiv.org/abs/2303.12776v2 | AP | 92.7 |
16k > Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | https://arxiv.org/abs/2303.12776v2 | mMR | 41.0 |
16k > Object Detection | CrowdHuman (full body) | DDQ FCN (R50 One-Stage) | https://arxiv.org/abs/2303.12776v2 | Recall | 98.2 |
16k > Object Detection | CrowdHuman (full body) | UniHCP (finetune) | https://arxiv.org/abs/2303.02936v4 | AP | 92.5 |
16k > Object Detection | CrowdHuman (full body) | UniHCP (finetune) | https://arxiv.org/abs/2303.02936v4 | mMR | 41.6 |
16k > Object Detection | CrowdHuman (full body) | Hulk(Finetune, ViT-B) | https://arxiv.org/abs/2312.01697v4 | AP | 92.4 |
16k > Object Detection | CrowdHuman (full body) | Hulk(Finetune, ViT-B) | https://arxiv.org/abs/2312.01697v4 | mMR | 40.7 |
16k > Object Detection | CrowdHuman (full body) | V2F-Net | https://arxiv.org/abs/2104.03106v1 | AP | 91.03 |
16k > Object Detection | CrowdHuman (full body) | V2F-Net | https://arxiv.org/abs/2104.03106v1 | mMR | 42.28 |
16k > Object Detection | CrowdHuman (full body) | V2F-Net | https://arxiv.org/abs/2104.03106v1 | Recall | 84.2 |
16k > Object Detection | CrowdHuman (full body) | CrowdDet | https://arxiv.org/abs/2003.09163v2 | AP | 90.7 |
16k > Object Detection | CrowdHuman (full body) | CrowdDet | https://arxiv.org/abs/2003.09163v2 | mMR | 41.4 |
16k > Object Detection | CrowdHuman (full body) | Beta R-CNN | https://arxiv.org/abs/2210.12758v1 | AP | 89.6 |
16k > Object Detection | CrowdHuman (full body) | Beta R-CNN | https://arxiv.org/abs/2210.12758v1 | mMR | 40.3 |
16k > Object Detection | CrowdHuman (full body) | NOH-NMS | https://arxiv.org/abs/2007.13376v1 | AP | 89.0 |
16k > Object Detection | CrowdHuman (full body) | NOH-NMS | https://arxiv.org/abs/2007.13376v1 | mMR | 43.9 |
16k > Object Detection | CrowdHuman (full body) | IterDet (Faster RCNN, ResNet50, 2 iterations) | https://arxiv.org/abs/2005.05708v2 | AP | 88.08 |
16k > Object Detection | CrowdHuman (full body) | IterDet (Faster RCNN, ResNet50, 2 iterations) | https://arxiv.org/abs/2005.05708v2 | mMR | 49.44 |
16k > Object Detection | CrowdHuman (full body) | PS-RCNN (Faster RCNN, ResNet50, COCO Instance Masks | https://arxiv.org/abs/2003.07080v1 | AP | 87.94 |
16k > Object Detection | CrowdHuman (full body) | PS-RCNN (Faster RCNN, ResNet50) | https://arxiv.org/abs/2003.07080v1 | AP | 86.05 |
16k > Object Detection | CrowdHuman (full body) | Faster RCNN (ResNet50) | http://arxiv.org/abs/1805.00123v1 | AP | 84.95 |
16k > Object Detection | CrowdHuman (full body) | Faster RCNN (ResNet50) | http://arxiv.org/abs/1805.00123v1 | mMR | 50.49 |
16k > Object Detection | CrowdHuman (full body) | Adaptive NMS (Faster RCNN, ResNet50) | http://arxiv.org/abs/1904.03629v1 | AP | 84.71 |
16k > Object Detection | CrowdHuman (full body) | Adaptive NMS (Faster RCNN, ResNet50) | http://arxiv.org/abs/1904.03629v1 | mMR | 49.73 |
16k > Object Detection | CrowdHuman (full body) | IterDet (Faster RCNN, ResNet50, 1 iteration) | https://arxiv.org/abs/2005.05708v2 | AP | 84.43 |
16k > Object Detection | CrowdHuman (full body) | IterDet (Faster RCNN, ResNet50, 1 iteration) | https://arxiv.org/abs/2005.05708v2 | mMR | 49.12 |
16k > Object Detection | UAVDT | PRB-FPN | https://arxiv.org/abs/2012.01724v5 | mAP | 76.55 |
16k > Object Detection | UAVDT | FFAVOD-SpotNet with U-Net | https://arxiv.org/abs/2109.07298v1 | mAP | 53.76 |
16k > Object Detection | UAVDT | SpotNet | https://arxiv.org/abs/2002.05540v2 | mAP | 52.8 |
16k > Object Detection | UAVDT | RN-VID | https://arxiv.org/abs/2003.10898v2 | mAP | 39.43 |
16k > Object Detection | UAVDT | R-FCN | http://arxiv.org/abs/1804.00518v1 | mAP | 34.35 |
16k > Object Detection | UAVDT | SSD | http://arxiv.org/abs/1804.00518v1 | mAP | 33.62 |
16k > Object Detection | UAVDT | Faster-RCNN | http://arxiv.org/abs/1804.00518v1 | mAP | 22.32 |
16k > Object Detection | UAVDT | RON | http://arxiv.org/abs/1804.00518v1 | mAP | 21.59 |
16k > Object Detection | KITTI Cars Moderate | Patches | https://arxiv.org/abs/1910.04093v1 | AP | 77.16 |
16k > Object Detection | KITTI Cars Moderate | PointRCNN Shi et al. (2019) | https://arxiv.org/abs/1812.04244v2 | AP | 75.76 |
16k > Object Detection | KITTI Cars Moderate | Vote3Deep | http://arxiv.org/abs/1609.06666v2 | AP | 68.24 |
16k > Object Detection | KITTI Cars Moderate | VeloFCN | http://arxiv.org/abs/1608.07916v1 | AP | 47.51 |
16k > Object Detection | FLIR | MiPa | https://arxiv.org/abs/2404.18849v2 | AP 0.5 | 0.813 |
16k > Object Detection | Waymo 2D detection all_ns test | UniverseNet | https://arxiv.org/abs/2103.14027v3 | AP/L2 | 67.42 |
16k > Object Detection | SpaceNet 2 | YOLT | https://arxiv.org/abs/1807.01232v3 | F1 Score (Avg. over Cities) | 0.60 |
16k > Object Detection | SpaceNet 2 | Multi-Task Network Cascades | https://arxiv.org/abs/1807.01232v3 | F1 Score (Avg. over Cities) | 0.57 |
16k > Object Detection | COCO (Common Objects in Context) | MOAT-3 22K+1K | https://arxiv.org/abs/2210.01820v2 | box AP | 59.2 |
16k > Object Detection | COCO (Common Objects in Context) | MOAT-2 | https://arxiv.org/abs/2210.01820v2 | box AP | 58.5 |
16k > Object Detection | COCO (Common Objects in Context) | REGO-Deformable DETR-X101 | https://arxiv.org/abs/2112.04632v2 | GFlops | 434 |
16k > Object Detection | MUSES: MUlti-SEnsor Semantic perception dataset | Mask2Former (R50) | https://arxiv.org/abs/2401.12761v4 | AP | 28.14 |
16k > Object Detection | DSEC | CAFR | https://arxiv.org/abs/2407.12582v2 | mAP | 38.0 |
16k > Object Detection | DSEC | EFNet | https://arxiv.org/abs/2112.00167v3 | mAP | 30.0 |
16k > Object Detection | DSEC | RENet | https://arxiv.org/abs/2209.08323v2 | mAP | 29.4 |
16k > Object Detection | DSEC | CMX | https://arxiv.org/abs/2203.04838v5 | mAP | 29.1 |
16k > Object Detection | DSEC | SPNet | https://arxiv.org/abs/2108.08162v2 | mAP | 27.7 |
16k > Object Detection | DSEC | SENet | https://arxiv.org/abs/1709.01507v4 | mAP | 26.2 |
16k > Object Detection | DSEC | CBAM | http://arxiv.org/abs/1807.06521v2 | mAP | 26.1 |
16k > Object Detection | DSEC | DCF | http://openaccess.thecvf.com//content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | mAP | 25.7 |
16k > Object Detection | DSEC | ECANet | https://arxiv.org/abs/1910.03151v4 | mAP | 25.7 |
16k > Object Detection | DSEC | FPN-Fusion | https://hal.archives-ouvertes.fr/hal-03591717/ | mAP | 24.4 |
16k > Object Detection | DSEC | SAGate | https://arxiv.org/abs/2007.09183v1 | mAP | 19.6 |
16k > Object Detection | DSEC | RAMNet | https://arxiv.org/abs/2102.09320v1 | mAP | 17.6 |
16k > Object Detection | BigDetection val | Cascade R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP | 24.1 |
16k > Object Detection | BigDetection val | Cascade R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP50 | 33.0 |
16k > Object Detection | BigDetection val | Cascade R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP75 | 25.8 |
16k > Object Detection | BigDetection val | CenterNet2 (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP | 23.1 |
16k > Object Detection | BigDetection val | CenterNet2 (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP50 | 30.2 |
16k > Object Detection | BigDetection val | CenterNet2 (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP75 | 24.9 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP | 19.4 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP50 | 29.3 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50-FPN) | https://arxiv.org/abs/2203.13249v1 | AP75 | 21.3 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50) | https://arxiv.org/abs/2203.13249v1 | AP | 18.9 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50) | https://arxiv.org/abs/2203.13249v1 | AP50 | 28.8 |
16k > Object Detection | BigDetection val | Faster R-CNN (R50) | https://arxiv.org/abs/2203.13249v1 | AP75 | 20.5 |
16k > Object Detection | BigDetection val | Deformable DETR (R50) | https://arxiv.org/abs/2203.13249v1 | AP | 13.1 |
16k > Object Detection | BigDetection val | Deformable DETR (R50) | https://arxiv.org/abs/2203.13249v1 | AP50 | 19.3 |
16k > Object Detection | BigDetection val | Deformable DETR (R50) | https://arxiv.org/abs/2203.13249v1 | AP75 | 14.2 |
16k > Object Detection | BigDetection val | YOLOv3 (D53) | https://arxiv.org/abs/2203.13249v1 | AP | 9.7 |
16k > Object Detection | BigDetection val | YOLOv3 (D53) | https://arxiv.org/abs/2203.13249v1 | AP50 | 17.4 |
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