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 > Weakly Supervised Object Detection | PASCAL VOC 2007 | ZLDN-L | http://arxiv.org/abs/1804.09466v1 | MAP | 47.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | MELM | http://arxiv.org/abs/1902.06057v1 | MAP | 47.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | OICR-Ens + FRCNN | http://arxiv.org/abs/1704.00138v1 | MAP | 47.0 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | OICR + W-RPN | http://openaccess.thecvf.com/content_CVPR_2019/html/Singh_You_Reap_What_You_Sow_Using_Videos_to_Generate_High_CVPR_2019_paper.html | MAP | 46.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | SegAgg-Adapt | https://www.sciencedirect.com/science/article/pii/S0166361519307559 | MAP | 46.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | VGPMIL | http://openaccess.thecvf.com/content_cvpr_2017/html/Haussmann_Variational_Bayesian_Multiple_CVPR_2017_paper.html | MAP | 46.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WebRelETH | http://arxiv.org/abs/1707.08721v2 | MAP | 46.0 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | SegAgg | http://bmvc2018.org/contents/papers/0557.pdf | MAP | 43.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Deep Self-Taught Learning | http://arxiv.org/abs/1704.05188v2 | MAP | 43.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WCCN | http://arxiv.org/abs/1611.08258v1 | MAP | 42.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | MSLPD | http://arxiv.org/abs/1706.08249v8 | MAP | 41.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Our scheme | https://arxiv.org/1910.02101 | MAP | 40.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WSDDN-Ens | http://arxiv.org/abs/1511.02853v4 | MAP | 39.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Self-Paced Learning | http://arxiv.org/abs/1605.07651v3 | MAP | 38.11 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | NSOD | https://arxiv.org/abs/1912.00384v6 | MAP | 38.0 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WSDDN + context | http://arxiv.org/abs/1609.04331v1 | MAP | 36.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Self-paced curriculum learning | http://arxiv.org/abs/1703.01290v1 | MAP | 31.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Cover + SLSVM | http://arxiv.org/abs/1403.1024v4 | MAP | 22.7 |
16k > Object Detection > Weakly Supervised Object Detection | COCO (Common Objects in Context) | MSLPD | http://arxiv.org/abs/1706.08249v8 | MAP | 56.6 |
16k > Object Detection > Weakly Supervised Object Detection | COCO (Common Objects in Context) | SPNs | http://arxiv.org/abs/1709.01829v1 | MAP | 55.3 |
16k > Object Detection > Weakly Supervised Object Detection | COCO (Common Objects in Context) | WILDCAT | http://openaccess.thecvf.com/content_cvpr_2017/html/Durand_WILDCAT_Weakly_Supervised_CVPR_2017_paper.html | MAP | 53.4 |
16k > Object Detection > Weakly Supervised Object Detection | COCO (Common Objects in Context) | Deep Feature Maps | http://arxiv.org/abs/1603.00489v2 | MAP | 47.9 |
16k > Object Detection > Weakly Supervised Object Detection | COCO (Common Objects in Context) | ProNet | http://arxiv.org/abs/1511.03776v3 | MAP | 43.5 |
16k > Object Detection > Weakly Supervised Object Detection | IconArt | MI_Net [wang_revisiting_2018] | https://arxiv.org/abs/2008.01178v5 | MAP | 15.1 |
16k > Object Detection > Weakly Supervised Object Detection | IconArt | MI-max-C | http://arxiv.org/abs/1810.02569v1 | MAP | 13.2 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2017 | OD-WSCL | https://arxiv.org/abs/2208.07576v2 | AP | 13.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-MIST-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 70.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-MIST-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 69.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-MIST (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 66.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-OICR-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 63.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-OICR-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 62.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WeakSAM-OICR (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 58.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | OD-WSCL | https://arxiv.org/abs/2208.07576v2 | MAP | 54.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | CASD(VGG16) | https://arxiv.org/abs/2010.12023v1 | MAP | 53.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | wetectron(single-model) | https://arxiv.org/abs/2004.04725v3 | MAP | 52.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | C-MIDN+FRCNN | http://openaccess.thecvf.com/content_ICCV_2019/html/Gao_C-MIDN_Coupled_Multiple_Instance_Detection_Network_With_Segmentation_Guidance_for_ICCV_2019_paper.html | MAP | 50.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Pred Net (Ens) | http://arxiv.org/abs/1811.10016v1 | MAP | 49.5 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Our-Ens | https://arxiv.org/abs/1911.12148v1 | MAP | 49.5 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Ours + FRCNN | https://arxiv.org/abs/1908.03792v1 | MAP | 48.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Ours+FRCNN | https://arxiv.org/abs/1906.06023v1 | MAP | 48.0 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WSD+PGE+PGA+FSD2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_W2F_A_Weakly-Supervised_CVPR_2018_paper.html | MAP | 47.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WSOD2 | https://arxiv.org/abs/1909.04972 | MAP | 47.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | C-MIL | http://arxiv.org/abs/1904.05647v1 | MAP | 46.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | OIM+IR+FRCNN | https://arxiv.org/abs/2002.01087v1 | MAP | 46.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WS-JDS FRCNN | http://openaccess.thecvf.com/content_CVPR_2019/html/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.html | MAP | 46.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | PCL-OB-G-Ens + FRCNN | http://arxiv.org/abs/1807.03342v2 | MAP | 44.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | OICR + W-RPN | http://openaccess.thecvf.com/content_CVPR_2019/html/Singh_You_Reap_What_You_Sow_Using_Videos_to_Generate_High_CVPR_2019_paper.html | MAP | 43.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | ZLDN-L | http://arxiv.org/abs/1804.09466v1 | MAP | 42.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WebRelETH | http://arxiv.org/abs/1707.08721v2 | MAP | 42.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | OICR-Ens + FRCNN | http://arxiv.org/abs/1704.00138v1 | MAP | 42.5 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | MELM | http://arxiv.org/abs/1902.06057v1 | MAP | 42.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Deep Self-Taught Learning | http://arxiv.org/abs/1704.05188v2 | MAP | 38.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WCCN | http://arxiv.org/abs/1611.08258v1 | MAP | 37.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | LM-VGPMIL | http://openaccess.thecvf.com/content_cvpr_2017/html/Haussmann_Variational_Bayesian_Multiple_CVPR_2017_paper.html | MAP | 37.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | NSOD | https://arxiv.org/abs/1912.00384v6 | MAP | 36.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | MSLPD | http://arxiv.org/abs/1706.08249v8 | MAP | 35.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | WSDDN + context | http://arxiv.org/abs/1609.04331v1 | MAP | 35.3 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2012 test | Our scheme | https://arxiv.org/1910.02101 | MAP | 35.2 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | Spatial Prior | http://arxiv.org/abs/1904.01665v1 | MAP | 10.03 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | PCL | http://arxiv.org/abs/1807.03342v2 | MAP | 2.83 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | TD-LSTM | http://arxiv.org/abs/1708.00666v1 | MAP | 1.98 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | ContextLocNet | http://arxiv.org/abs/1609.04331v1 | MAP | 1.12 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | R*CNN | http://arxiv.org/abs/1505.01197v3 | MAP | 0.99 |
16k > Object Detection > Weakly Supervised Object Detection | Charades | WSDDN | http://arxiv.org/abs/1511.02853v4 | MAP | 0.65 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | TADP | https://arxiv.org/abs/2310.00031v3 | MAP | 72.2 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | DASS-Detector (YOLOX Tiny) | https://arxiv.org/abs/2211.10641v2 | MAP | 71.53 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | H2FA R-CNN (+extra) | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.html | MAP | 62.6 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | H2FA R-CNN | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.html | MAP | 59.9 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | DT+PL (+extra) | http://arxiv.org/abs/1803.11365v1 | MAP | 59.1 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | ICCM | http://openaccess.thecvf.com//content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | MAP | 57.4 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | MCAR | https://arxiv.org/abs/2003.12943v2 | MAP | 56.0 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | MEAA | https://dl.acm.org/doi/10.1145/3394171.3413553 | MAP | 55.5 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | DT+PL | http://arxiv.org/abs/1803.11365v1 | MAP | 54.3 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | MI-max | http://arxiv.org/abs/1810.02569v1 | MAP | 50.1 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | MI-max | https://arxiv.org/abs/2008.01178v5 | MAP | 49.5 |
16k > Object Detection > Weakly Supervised Object Detection | Watercolor2k | WSDDN | http://arxiv.org/abs/1511.02853v4 | MAP | 12.7 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | H2FA R-CNN (clipart_all) | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.html | MAP | 69.8 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | DASS-Detector (YOLOX Tiny) | https://arxiv.org/abs/2211.10641v2 | MAP | 64.25 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | H2FA R-CNN (clipart_test) | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_H2FA_R-CNN_Holistic_and_Hierarchical_Feature_Alignment_for_Cross-Domain_Weakly_CVPR_2022_paper.html | MAP | 55.3 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | ICCM | http://openaccess.thecvf.com//content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | MAP | 46.7 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | DT+PL | http://arxiv.org/abs/1803.11365v1 | MAP | 46.0 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | MEAA | https://dl.acm.org/doi/10.1145/3394171.3413553 | MAP | 41.1 |
16k > Object Detection > Weakly Supervised Object Detection | Clipart1k | MI-max | https://arxiv.org/abs/2008.01178v5 | MAP | 38.4 |
16k > Object Detection > Weakly Supervised Object Detection | COCO test-dev | wetectron(single-model, VGG16) | https://arxiv.org/abs/2004.04725v3 | AP50 | 24.8 |
16k > Object Detection > Weakly Supervised Object Detection | COCO test-dev | WSGARN+SSD | http://arxiv.org/abs/1711.08174v2 | AP50 | 13.6 |
16k > Object Detection > Weakly Supervised Object Detection | COCO test-dev | WCCN | http://arxiv.org/abs/1611.08258v1 | AP50 | 12.3 |
16k > Object Detection > Weakly Supervised Object Detection | COCO test-dev | WSDDN | http://arxiv.org/abs/1511.02853v4 | AP50 | 11.5 |
16k > Object Detection > Weakly Supervised Object Detection | Cityscapes-to-Foggy Cityscapes | MEAA | https://dl.acm.org/doi/10.1145/3394171.3413553 | mAP | 40.5 |
16k > Object Detection > Weakly Supervised Object Detection | Cityscapes-to-Foggy Cityscapes | HTCN | https://arxiv.org/abs/2003.06297v1 | mAP | 39.8 |
16k > Object Detection > Weakly Supervised Object Detection | ImageNet | PCL-OB-G-Ens + FRCNN | http://arxiv.org/abs/1807.03342v2 | MAP | 19.6 |
16k > Object Detection > Weakly Supervised Object Detection | ImageNet | WCCN | http://arxiv.org/abs/1611.08258v1 | MAP | 16.3 |
16k > Object Detection > Weakly Supervised Object Detection | ImageNet | MSLPD | http://arxiv.org/abs/1706.08249v8 | MAP | 13.9 |
16k > Object Detection > Weakly Supervised Object Detection | ImageNet | Online Instance Classifier Refinement | http://arxiv.org/abs/1704.00138v1 | MAP | 6 |
16k > Object Detection > Weakly Supervised Object Detection | PeopleArt | Polyhedral MI-max | https://arxiv.org/abs/2008.01178v5 | MAP | 58.3 |
16k > Object Detection > Weakly Supervised Object Detection | PeopleArt | MI-max | http://arxiv.org/abs/1810.02569v1 | MAP | 55.4 |
16k > Object Detection > Robust Object Detection | PASCAL VOC 2007 | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | mPC [AP50] | 56.2 |
16k > Object Detection > Robust Object Detection | PASCAL VOC 2007 | Faster R-CNN with Stylized Training Data | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 69.9 |
16k > Object Detection > Robust Object Detection | PASCAL VOC 2007 | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | mPC [AP50] | 48.6 |
16k > Object Detection > Robust Object Detection | PASCAL VOC 2007 | Faster R-CNN | https://arxiv.org/abs/1907.07484v2 | rPC [%] | 60.4 |
16k > Object Detection > Robust Object Detection | Cityscapes | FGT (SD-1.5 Backbone) | https://arxiv.org/abs/2506.21042v1 | mPC [AP] | 27.4 |
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