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 > Robust Object Detection | Cityscapes | GDD (SD-1.5 Backbone) | https://arxiv.org/abs/2503.02101v1 | mPC [AP] | 24.1 |
16k > Object Detection > Robust Object Detection | Cityscapes | PhysAug | https://arxiv.org/abs/2412.11807v1 | mPC [AP] | 22.6 |
16k > Object Detection > Robust Object Detection | Cityscapes | FGT (R101, Faster RCNN) | https://arxiv.org/abs/2506.21042v1 | mPC [AP] | 22.1 |
16k > Object Detection > Robust Object Detection | Cityscapes | OA-DG | https://arxiv.org/abs/2312.12133v1 | mPC [AP] | 21.8 |
16k > Object Detection > Robust Object Detection | Cityscapes | GDD (R101, Faster RCNN) | https://arxiv.org/abs/2503.02101v1 | mPC [AP] | 21.3 |
16k > Object Detection > Robust Object Detection | Cityscapes | OA-Mix | https://arxiv.org/abs/2312.12133v1 | mPC [AP] | 20.8 |
16k > Object Detection > Robust Object Detection | Cityscapes | AugMix | https://arxiv.org/abs/1912.02781v2 | mPC [AP] | 18.1 |
16k > Object Detection > Robust Object Detection | Cityscapes | Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | mPC [AP] | 17.2 |
16k > Object Detection > Robust Object Detection | Cityscapes | Photometric distortion | http://arxiv.org/abs/1804.02767v1 | mPC [AP] | 16.9 |
16k > Object Detection > Robust Object Detection | Cityscapes | AutoAug-det | https://arxiv.org/abs/1906.11172v1 | mPC [AP] | 15.8 |
16k > Object Detection > Robust Object Detection | Cityscapes | Cutout | http://arxiv.org/abs/1708.04896v2 | mPC [AP] | 15.7 |
16k > Object Detection > Robust Object Detection | Cityscapes | Baseline | http://arxiv.org/abs/1506.01497v3 | mPC [AP] | 15.4 |
16k > Object Detection > Robust Object Detection | Cityscapes test | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | mPC [AP] | 17.2 |
16k > Object Detection > Robust Object Detection | Cityscapes test | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 47.4 |
16k > Object Detection > Robust Object Detection | Cityscapes test | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | mPC [AP] | 12.2 |
16k > Object Detection > Robust Object Detection | Cityscapes test | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 33.4 |
16k > Object Detection > Robust Object Detection | DWD | GDD (SD-1.5 Backbone) | https://arxiv.org/abs/2503.02101v1 | mPC [AP50] | 40.5 |
16k > Object Detection > Robust Object Detection | DWD | GDD (R101, Faster RCNN) | https://arxiv.org/abs/2503.02101v1 | mPC [AP50] | 38.1 |
16k > Object Detection > Robust Object Detection | DWD | PhysAug | https://arxiv.org/abs/2412.11807v1 | mPC [AP50] | 37.5 |
16k > Object Detection > Robust Object Detection | DWD | VLTDet | https://arxiv.org/abs/2312.02021v4 | mPC [AP50] | 36.9 |
16k > Object Detection > Robust Object Detection | DWD | OA-DG | https://arxiv.org/abs/2312.12133v1 | mPC [AP50] | 31.8 |
16k > Object Detection > Robust Object Detection | DWD | SRCD | https://arxiv.org/abs/2307.01750v2 | mPC [AP50] | 29.6 |
16k > Object Detection > Robust Object Detection | DWD | CDSD | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Single-Domain_Generalized_Object_Detection_in_Urban_Scene_via_Cyclic-Disentangled_Self-Distillation_CVPR_2022_paper.html | mPC [AP50] | 28.7 |
16k > Object Detection > Robust Object Detection | DWD | SHADE | https://arxiv.org/abs/2204.02548v2 | mPC [AP50] | 28.4 |
16k > Object Detection > Robust Object Detection | DWD | ISW | https://arxiv.org/abs/2103.15597v2 | mPC [AP50] | 26.3 |
16k > Object Detection > Robust Object Detection | DWD | SW | https://arxiv.org/abs/1904.09739v4 | mPC [AP50] | 26.1 |
16k > Object Detection > Robust Object Detection | DWD | IBN-Net | https://arxiv.org/abs/1807.09441v3 | mPC [AP50] | 25.5 |
16k > Object Detection > Robust Object Detection | DWD | IterNorm | http://arxiv.org/abs/1904.03441v1 | mPC [AP50] | 23.4 |
16k > Object Detection > Robust Object Detection | COCO (Common Objects in Context) | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | mPC [AP] | 20.4 |
16k > Object Detection > Robust Object Detection | COCO (Common Objects in Context) | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 58.9 |
16k > Object Detection > Robust Object Detection | COCO (Common Objects in Context) | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | mPC [AP] | 18.2 |
16k > Object Detection > Robust Object Detection | COCO (Common Objects in Context) | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 50.2 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | CP-DETR-Pro(without LVIS data) | https://arxiv.org/abs/2412.09799v1 | AP | 58.2 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | Grounding DINO 1.6 Pro (without LVIS data) | https://arxiv.org/abs/2405.10300v2 | AP | 57.7 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | Grounding DINO 1.5 Pro (without LVIS data) | https://arxiv.org/abs/2405.10300v2 | AP | 55.7 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | OWLv2 (OWL-ST+FT) | https://arxiv.org/abs/2306.09683v3 | AP | 51.3 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | MQ-GLIP-L | https://arxiv.org/abs/2305.18980v2 | AP | 43.4 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | OV-DINO-T (without LVIS data, swin tiny) | https://arxiv.org/abs/2407.07844v2 | AP | 40.1 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | GLIP-L | https://arxiv.org/abs/2112.03857v2 | AP | 37.3 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | YOLO-World-L | https://arxiv.org/abs/2401.17270v3 | AP | 35.4 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | GroundingDINO-L | https://arxiv.org/abs/2303.05499v5 | AP | 33.9 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | MQ-GLIP-T | https://arxiv.org/abs/2305.18980v2 | AP | 30.4 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 minival | MQ-GroundingDINO-T | https://arxiv.org/abs/2305.18980v2 | AP | 30.2 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | Grounding DINO 1.6 Pro (without COCO data) | https://arxiv.org/abs/2405.10300v2 | AP | 55.4 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | CP-DETR-Pro((without COCO data)) | https://arxiv.org/abs/2412.09799v1 | AP | 55.4 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | Grounding DINO 1.5 Pro (without COCO data) | https://arxiv.org/abs/2405.10300v2 | AP | 54.3 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | Grounding DINO-L (without COCO data) | https://arxiv.org/abs/2303.05499v5 | AP | 52.5 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | OV-DINO-T (without COCO data) | https://arxiv.org/abs/2407.07844v2 | AP | 50.6 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | YOLO-World-L(without COCO data) | https://arxiv.org/abs/2401.17270v3 | AP | 45.1 |
16k > Object Detection > Zero-Shot Object Detection | MSCOCO | Object-Centric-OVD | https://arxiv.org/abs/2207.03482v3 | AP | 40.5 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | CP-DETR-Pro(without LVIS data) | https://arxiv.org/abs/2412.09799v1 | AP | 51.6 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | Grounding DINO 1.6 Pro (without LVIS data) | https://arxiv.org/abs/2405.10300v2 | AP | 51.1 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | Grounding DINO 1.5 Pro (without LVIS data) | https://arxiv.org/abs/2405.10300v2 | AP | 47.7 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | OWLv2 (OWL-ST+FT) | https://arxiv.org/abs/2306.09683v3 | AP | 47.0 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | MQ-GLIP-L | https://arxiv.org/abs/2305.18980v2 | AP | 34.7 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | OV-DINO-T (without LVIS data, swin tiny) | https://arxiv.org/abs/2407.07844v2 | AP | 32.9 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | GLIP-L | https://arxiv.org/abs/2112.03857v2 | AP | 26.9 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | MQ-GLIP-T | https://arxiv.org/abs/2305.18980v2 | AP | 22.6 |
16k > Object Detection > Zero-Shot Object Detection | LVIS v1.0 val | MQ-GroundingDINO-T | https://arxiv.org/abs/2305.18980v2 | AP | 22.1 |
16k > Object Detection > Zero-Shot Object Detection | ImageNet Detection | SUZOD | https://arxiv.org/abs/2010.09425v1 | mAP | 24.3 |
16k > Object Detection > Zero-Shot Object Detection | ODinW | CP-DETR-L Swin-L | https://arxiv.org/abs/2412.09799v1 | Average Score | 32.2 |
16k > Object Detection > Zero-Shot Object Detection | ODinW | Grounding DINO 1.5 Pro | https://arxiv.org/abs/2405.10300v2 | Average Score | 30.2 |
16k > Object Detection > Zero-Shot Object Detection | ODinW | Grounding DINO | https://arxiv.org/abs/2303.05499v5 | Average Score | 26.1 |
16k > Object Detection > Zero-Shot Object Detection | ODinW | MQ-GLIP-L | https://arxiv.org/abs/2305.18980v2 | Average Score | 23.9 |
16k > Object Detection > Zero-Shot Object Detection | ODinW | GLIP (Tiny A) | https://arxiv.org/abs/2204.08790v6 | Average Score | 11.4 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | UniFa | https://www.sciencedirect.com/science/article/abs/pii/S0167865525000194 | mAP | 26.00 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | UniFa | https://www.sciencedirect.com/science/article/abs/pii/S0167865525000194 | Recall | 69.10 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | SeeDS | https://arxiv.org/abs/2310.04689v1 | mAP | 20.6 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | SeeDS | https://arxiv.org/abs/2310.04689v1 | Recall | 64 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-SCR | https://arxiv.org/abs/2212.06097v1 | mAP | 20.10 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-SCR | https://arxiv.org/abs/2212.06097v1 | Recall | 65.10 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-RRFS | https://arxiv.org/abs/2201.00103v1 | mAP | 19.8 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-RRFS | https://arxiv.org/abs/2201.00103v1 | Recall | 62.3 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ContrastZSD | https://arxiv.org/abs/2109.06062v2 | mAP | 18.60 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ContrastZSD | https://arxiv.org/abs/2109.06062v2 | Recall | 59.50 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | SUZOD | https://arxiv.org/abs/2010.09425v1 | mAP | 17.30 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | SUZOD | https://arxiv.org/abs/2010.09425v1 | Recall | 61.40 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | GRAN | https://ieeexplore.ieee.org/document/9706663 | mAP | 14.90 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | GRAN | https://ieeexplore.ieee.org/document/9706663 | Recall | 62.70 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | BLC | https://arxiv.org/abs/2010.04502v1 | mAP | 14.70 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | BLC | https://arxiv.org/abs/2010.04502v1 | Recall | 54.68 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-Polarity Loss | https://arxiv.org/abs/1811.08982v3 | mAP | 12.62 |
16k > Object Detection > Zero-Shot Object Detection | MS-COCO | ZSD-Polarity Loss | https://arxiv.org/abs/1811.08982v3 | Recall | 43.56 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | SeeDS | https://arxiv.org/abs/2310.04689v1 | mAP | 68.9 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | RRFS-ZSD | https://arxiv.org/abs/2201.00103v1 | mAP | 65.50 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | SUZOD | https://arxiv.org/abs/2010.09425v1 | mAP | 64.9 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | ZSD-SCR | https://arxiv.org/abs/2212.06097v1 | mAP | 62.70 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | PL | https://arxiv.org/abs/1811.08982v3 | mAP | 62.10 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | BLC | https://arxiv.org/abs/2010.04502v1 | mAP | 55.20 |
16k > Object Detection > Zero-Shot Object Detection | PASCAL VOC'07 | HRE | http://arxiv.org/abs/1805.06157v2 | mAP | 54.20 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | ZS-VCOS | https://www.researchgate.net/publication/390322532_ZS-VCOS_Zero-Shot_Outperforms_Supervised_Video_Camouflaged_Object_Segmentation_with_Zero-Shot_Method | S-measure | 0.776 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | ZS-VCOS | https://www.researchgate.net/publication/390322532_ZS-VCOS_Zero-Shot_Outperforms_Supervised_Video_Camouflaged_Object_Segmentation_with_Zero-Shot_Method | weighted F-measure | 0.628 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | ZS-VCOS | https://www.researchgate.net/publication/390322532_ZS-VCOS_Zero-Shot_Outperforms_Supervised_Video_Camouflaged_Object_Segmentation_with_Zero-Shot_Method | MAE | 0.008 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | ZS-VCOS | https://www.researchgate.net/publication/390322532_ZS-VCOS_Zero-Shot_Outperforms_Supervised_Video_Camouflaged_Object_Segmentation_with_Zero-Shot_Method | mDice | 0.648 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | ZS-VCOS | https://www.researchgate.net/publication/390322532_ZS-VCOS_Zero-Shot_Outperforms_Supervised_Video_Camouflaged_Object_Segmentation_with_Zero-Shot_Method | mIoU | 0.550 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | CamoSAM2 | https://arxiv.org/abs/2504.00375v1 | S-measure | 0.765 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | CamoSAM2 | https://arxiv.org/abs/2504.00375v1 | weighted F-measure | 0.607 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | CamoSAM2 | https://arxiv.org/abs/2504.00375v1 | MAE | 0.007 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | CamoSAM2 | https://arxiv.org/abs/2504.00375v1 | mDice | 0.62 |
16k > Object Detection > Camouflaged Object Segmentation | MoCA-Mask | CamoSAM2 | https://arxiv.org/abs/2504.00375v1 | mIoU | 0.542 |
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