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 > RGB-D Salient Object Detection | DES | DFormer-L | https://arxiv.org/abs/2309.09668v2 | max F-Measure | 95.6 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BTS-Net | https://arxiv.org/abs/2104.01784v1 | S-Measure | 94.3 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BTS-Net | https://arxiv.org/abs/2104.01784v1 | Average MAE | 0.018 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BTS-Net | https://arxiv.org/abs/2104.01784v1 | max E-Measure | 97.9 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BTS-Net | https://arxiv.org/abs/2104.01784v1 | max F-Measure | 94.0 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UCNet-ABP | https://arxiv.org/abs/2009.03075v1 | S-Measure | 94.0 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UCNet-ABP | https://arxiv.org/abs/2009.03075v1 | Average MAE | 0.016 |
16k > Object Detection > RGB-D Salient Object Detection | DES | SPSN | https://arxiv.org/abs/2207.07898v1 | S-Measure | 93.8 |
16k > Object Detection > RGB-D Salient Object Detection | DES | SPSN | https://arxiv.org/abs/2207.07898v1 | Average MAE | 0.016 |
16k > Object Detection > RGB-D Salient Object Detection | DES | SPSN | https://arxiv.org/abs/2207.07898v1 | max E-Measure | 97.6 |
16k > Object Detection > RGB-D Salient Object Detection | DES | SPSN | https://arxiv.org/abs/2207.07898v1 | max F-Measure | 94.3 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UCNet-CVAE | https://arxiv.org/abs/2009.03075v1 | S-Measure | 93.7 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UCNet-CVAE | https://arxiv.org/abs/2009.03075v1 | Average MAE | 0.016 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF* | https://arxiv.org/abs/2008.12134v2 | S-Measure | 93.6 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF* | https://arxiv.org/abs/2008.12134v2 | Average MAE | 0.021 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF* | https://arxiv.org/abs/2008.12134v2 | max E-Measure | 97.5 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF* | https://arxiv.org/abs/2008.12134v2 | max F-Measure | 92.9 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CoLANet | https://arxiv.org/abs/2407.06780v1 | S-Measure | 93.5 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CoLANet | https://arxiv.org/abs/2407.06780v1 | Average MAE | 0.018 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CoLANet | https://arxiv.org/abs/2407.06780v1 | max E-Measure | 96.3 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CoLANet | https://arxiv.org/abs/2407.06780v1 | max F-Measure | 92.5 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UC-Net | https://arxiv.org/abs/2004.05763v1 | S-Measure | 93.4 |
16k > Object Detection > RGB-D Salient Object Detection | DES | UC-Net | https://arxiv.org/abs/2004.05763v1 | Average MAE | 0.019 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BBS-Net | https://arxiv.org/abs/2007.02713v3 | S-Measure | 93.3 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BBS-Net | https://arxiv.org/abs/2007.02713v3 | Average MAE | 0.021 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BBS-Net | https://arxiv.org/abs/2007.02713v3 | max E-Measure | 96.6 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BBS-Net | https://arxiv.org/abs/2007.02713v3 | max F-Measure | 92.7 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BiANet | https://arxiv.org/abs/2004.14582v1 | S-Measure | 93.1 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BiANet | https://arxiv.org/abs/2004.14582v1 | Average MAE | 0.021 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BiANet | https://arxiv.org/abs/2004.14582v1 | max E-Measure | 97.1 |
16k > Object Detection > RGB-D Salient Object Detection | DES | BiANet | https://arxiv.org/abs/2004.14582v1 | max F-Measure | 92.6 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF | https://arxiv.org/abs/2004.08515v1 | S-Measure | 92.9 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF | https://arxiv.org/abs/2004.08515v1 | Average MAE | 0.022 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF | https://arxiv.org/abs/2004.08515v1 | max E-Measure | 96.8 |
16k > Object Detection > RGB-D Salient Object Detection | DES | JL-DCF | https://arxiv.org/abs/2004.08515v1 | max F-Measure | 91.9 |
16k > Object Detection > RGB-D Salient Object Detection | DES | DASNet | https://arxiv.org/abs/2006.00269v2 | S-Measure | 90.8 |
16k > Object Detection > RGB-D Salient Object Detection | DES | DASNet | https://arxiv.org/abs/2006.00269v2 | Average MAE | 0.023 |
16k > Object Detection > RGB-D Salient Object Detection | DES | DASNet | https://arxiv.org/abs/2006.00269v2 | max F-Measure | 92.8 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CPFP | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.html | S-Measure | 87.2 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CPFP | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.html | Average MAE | 0.038 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CPFP | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.html | max E-Measure | 92.3 |
16k > Object Detection > RGB-D Salient Object Detection | DES | CPFP | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Contrast_Prior_and_Fluid_Pyramid_Integration_for_RGBD_Salient_Object_CVPR_2019_paper.html | max F-Measure | 84.6 |
16k > Object Detection > Small Object Detection | SOD4SB Private Test | Weighted Box Fusion (WBF) | https://ieeexplore.ieee.org/document/10215748 | AP50 | 30.3 |
16k > Object Detection > Small Object Detection | SOD4SB Private Test | GFL + Test Time Augmentation | https://arxiv.org/abs/2307.11748v1 | AP50 | 23.7 |
16k > Object Detection > Small Object Detection | SOD4SB Private Test | DL method (YOLOv8 + Ensamble) | https://arxiv.org/abs/2307.09143v1 | AP50 | 22.9 |
16k > Object Detection > Small Object Detection | SOD4SB Private Test | Swin Transformer + Hierarchical design | https://ieeexplore.ieee.org/document/10216093 | AP50 | 22.6 |
16k > Object Detection > Small Object Detection | SOD4SB Private Test | E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) | https://arxiv.org/abs/2307.09143v1 | AP50 | 22.1 |
16k > Object Detection > Small Object Detection | Bee4Exp Honeybee Detection | BeeDetector | https://www.mdpi.com/2072-4292/13/4/653 | Average F1 | 0.86 |
16k > Object Detection > Small Object Detection | SODA-D | CFINet | https://arxiv.org/abs/2308.09534v1 | mAP@0.5:0.95 | 30.7 |
16k > Object Detection > Small Object Detection | SOD4SB Public Test | Weighted Box Fusion (WBF) | https://ieeexplore.ieee.org/document/10215748 | AP50 | 77.6 |
16k > Object Detection > Small Object Detection | SOD4SB Public Test | GFL + Test Time Augmentation | https://arxiv.org/abs/2307.11748v1 | AP50 | 73.1 |
16k > Object Detection > Small Object Detection | SOD4SB Public Test | DL method (YOLOv8 + Ensamble) | https://arxiv.org/abs/2307.09143v1 | AP50 | 73.1 |
16k > Object Detection > Small Object Detection | SOD4SB Public Test | Swin Transformer + Hierarchical design | https://ieeexplore.ieee.org/document/10216093 | AP50 | 70.2 |
16k > Object Detection > Small Object Detection | SOD4SB Public Test | E2 method (Normalized Gaussian Wasserstein Distance + Switch Hard Augmentation + Multi scale train + Weight Moving Average + CenterNet + VarifocalNet) | https://arxiv.org/abs/2307.09143v1 | AP50 | 69.6 |
16k > Object Detection > Small Object Detection > Rice Grain Disease Detection | Rice Grain Disease Dataset | Mini project | https://arxiv.org/abs/2004.09870v2 | mAP | 88.24 |
16k > Object Detection > Weakly Supervised Object Detection | MSCOCO | CASD(ResNet50) | https://arxiv.org/abs/2010.12023v1 | mAP | 13.9 |
16k > Object Detection > Weakly Supervised Object Detection | MSCOCO | CASD(ResNet50) | https://arxiv.org/abs/2010.12023v1 | mAP@50 | 27.8 |
16k > Object Detection > Weakly Supervised Object Detection | HICO-DET | Spatial Prior | http://arxiv.org/abs/1904.01665v1 | MAP | 5.39 |
16k > Object Detection > Weakly Supervised Object Detection | HICO-DET | PCL | http://arxiv.org/abs/1807.03342v2 | MAP | 3.62 |
16k > Object Detection > Weakly Supervised Object Detection | HICO-DET | WSDDN | http://arxiv.org/abs/1511.02853v4 | MAP | 3.27 |
16k > Object Detection > Weakly Supervised Object Detection | HICO-DET | R*CNN | http://arxiv.org/abs/1505.01197v3 | MAP | 2.15 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | DASS-Detector (YOLOX Tiny) | https://arxiv.org/abs/2211.10641v2 | MAP | 67.41 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | TADP | https://arxiv.org/abs/2310.00031v3 | MAP | 57.4 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | 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 | 53.0 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | 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 | 46.4 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | DT+PL (+extra) | http://arxiv.org/abs/1803.11365v1 | MAP | 42.2 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | DT+PL | http://arxiv.org/abs/1803.11365v1 | MAP | 37.2 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | 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 | 37.1 |
16k > Object Detection > Weakly Supervised Object Detection | Comic2k | MI-max | https://arxiv.org/abs/2008.01178v5 | MAP | 27 |
16k > Object Detection > Weakly Supervised Object Detection | CASPAPaintings | MI-max | https://arxiv.org/abs/2008.01178v5 | Mean mAP | 16.2 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-MIST-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 26.6 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-OICR-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 24.9 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-MIST-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 23.8 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-MIST (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 22.9 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-OICR-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 22.3 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | WeakSAM-OICR (with SAM) | https://arxiv.org/abs/2402.14812v2 | AP | 19.9 |
16k > Object Detection > Weakly Supervised Object Detection | MS-COCO-2014 | OD-WSCL | https://arxiv.org/abs/2208.07576v2 | AP | 13.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-MIST-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 73.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-MIST-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 71.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-MIST (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 67.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-OICR-DINO (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 66.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-OICR-Faster RCNN (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 65.7 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WeakSAM-OICR (with SAM) | https://arxiv.org/abs/2402.14812v2 | MAP | 58.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | wetectron (single mode, 07+12) | https://arxiv.org/abs/2004.04725v3 | MAP | 58.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | CASD(VGG16) | https://arxiv.org/abs/2010.12023v1 | MAP | 56.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | OD-WSCL | https://arxiv.org/abs/2208.07576v2 | MAP | 56.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | wetectron(single-model) | https://arxiv.org/abs/2004.04725v3 | MAP | 54.9 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Our-Ens | https://arxiv.org/abs/1911.12148v1 | MAP | 54.5 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Pred Net (Ens) | http://arxiv.org/abs/1811.10016v1 | MAP | 53.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WSOD2 | https://arxiv.org/abs/1909.04972 | MAP | 53.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | C-MIDN | 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 | 53.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | FRCNN C-MIL | http://arxiv.org/abs/1904.05647v1 | MAP | 53.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Ours+FRCNN | https://arxiv.org/abs/1906.06023v1 | MAP | 52.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | OIM+IR+FRCNN | https://arxiv.org/abs/2002.01087v1 | MAP | 52.6 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | 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 | 52.5 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | WSD+PGE+PGA+FSD2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_W2F_A_Weakly-Supervised_CVPR_2018_paper.html | MAP | 52.4 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | PGE (Ours + FRCNN) | https://arxiv.org/abs/1908.03792v1 | MAP | 52.1 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | pipeline method | http://arxiv.org/abs/1802.09129v1 | MAP | 51.2 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | PCL-OB-G-Ens + FRCNN | http://arxiv.org/abs/1807.03342v2 | MAP | 48.8 |
16k > Object Detection > Weakly Supervised Object Detection | PASCAL VOC 2007 | Ours+PRS | https://arxiv.org/abs/1911.11512v1 | MAP | 48.8 |
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