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 > Camouflaged Object Segmentation | CAMO | ZoomNeXt-ResNet-50 | https://arxiv.org/abs/2310.20208v4 | S-Measure | 0.833 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet-V2 | https://arxiv.org/abs/2102.10274v2 | MAE | 0.070 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet-V2 | https://arxiv.org/abs/2102.10274v2 | Weighted F-Measure | 0.743 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet-V2 | https://arxiv.org/abs/2102.10274v2 | S-Measure | 0.820 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | MirrorNet-ResNeXt152 | https://arxiv.org/abs/2007.12881v3 | MAE | 0.077 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | MirrorNet-ResNeXt152 | https://arxiv.org/abs/2007.12881v3 | Weighted F-Measure | 0.719 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | MirrorNet-ResNeXt152 | https://arxiv.org/abs/2007.12881v3 | S-Measure | 0.785 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | PraNet | https://arxiv.org/abs/2006.11392v4 | MAE | 0.094 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | PraNet | https://arxiv.org/abs/2006.11392v4 | Weighted F-Measure | 0.663 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | PraNet | https://arxiv.org/abs/2006.11392v4 | S-Measure | 0.769 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet | http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | MAE | 0.100 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet | http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | Weighted F-Measure | 0.606 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SINet | http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | S-Measure | 0.751 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | EGNet | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_EGNet_Edge_Guidance_Network_for_Salient_Object_Detection_ICCV_2019_paper.html | MAE | 0.104 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | EGNet | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_EGNet_Edge_Guidance_Network_for_Salient_Object_Detection_ICCV_2019_paper.html | Weighted F-Measure | 0.583 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | EGNet | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhao_EGNet_Edge_Guidance_Network_for_Salient_Object_Detection_ICCV_2019_paper.html | S-Measure | 0.732 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | BASNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.html | MAE | 0.159 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | BASNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.html | Weighted F-Measure | 0.413 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | BASNet | http://openaccess.thecvf.com/content_CVPR_2019/html/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.html | S-Measure | 0.618 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SAMFusion | https://www.preprints.org/manuscript/202307.1729/v1 | MAE | 0.0560 |
16k > Object Detection > Camouflaged Object Segmentation | CAMO | SAMFusion | https://www.preprints.org/manuscript/202307.1729/v1 | Weighted F-Measure | 0.833 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | S-measure | 0.757 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | weighted F-measure | 0.593 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | MAE | 0.020 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | mDice | 0.599 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | mIoU | 0.510 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | STL-Net-LT-PVTv2-B5 | https://arxiv.org/abs/2203.07363v2 | S-measure | 0.696 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | STL-Net-LT-PVTv2-B5 | https://arxiv.org/abs/2203.07363v2 | weighted F-measure | 0.481 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | STL-Net-LT-PVTv2-B5 | https://arxiv.org/abs/2203.07363v2 | MAE | 0.030 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | STL-Net-LT-PVTv2-B5 | https://arxiv.org/abs/2203.07363v2 | mDice | 0.493 |
16k > Object Detection > Camouflaged Object Segmentation | Camouflaged Animal Dataset | STL-Net-LT-PVTv2-B5 | https://arxiv.org/abs/2203.07363v2 | mIoU | 0.402 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | FOCUS | https://arxiv.org/abs/2501.05238v1 | S-measure | 0.915 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | FOCUS | https://arxiv.org/abs/2501.05238v1 | weighted F-measure | 0.906 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | FOCUS | https://arxiv.org/abs/2501.05238v1 | MAE | 0.020 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | BiRefNet | https://arxiv.org/abs/2401.03407v6 | S-measure | 0.914 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | BiRefNet | https://arxiv.org/abs/2401.03407v6 | weighted F-measure | 0.894 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | BiRefNet | https://arxiv.org/abs/2401.03407v6 | MAE | 0.023 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | S-measure | 0.903 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | weighted F-measure | 0.863 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B5 | https://arxiv.org/abs/2310.20208v4 | MAE | 0.028 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B4 | https://arxiv.org/abs/2310.20208v4 | S-measure | 0.900 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B4 | https://arxiv.org/abs/2310.20208v4 | weighted F-measure | 0.865 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-PVTv2-B4 | https://arxiv.org/abs/2310.20208v4 | MAE | 0.028 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-ResNet-50 | https://arxiv.org/abs/2310.20208v4 | S-measure | 0.874 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-ResNet-50 | https://arxiv.org/abs/2310.20208v4 | weighted F-measure | 0.816 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | ZoomNeXt-ResNet-50 | https://arxiv.org/abs/2310.20208v4 | MAE | 0.037 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | SINetV2-Res2Net-50 | https://arxiv.org/abs/2102.10274v2 | S-measure | 0.847 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | SINetV2-Res2Net-50 | https://arxiv.org/abs/2102.10274v2 | weighted F-measure | 0.770 |
16k > Object Detection > Camouflaged Object Segmentation | NC4K | SINetV2-Res2Net-50 | https://arxiv.org/abs/2102.10274v2 | MAE | 0.048 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | RDVP-MSD | null | MAE | 0.081 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | RDVP-MSD | null | E_{\phi} | 0.848 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | RDVP-MSD | null | S_{\alpha} | 0.796 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | ProMaC | https://arxiv.org/abs/2408.15205v3 | MAE | 0.09 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | ProMaC | https://arxiv.org/abs/2408.15205v3 | F_{\beta} | 0.725 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | ProMaC | https://arxiv.org/abs/2408.15205v3 | E_{\phi} | 0.846 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | ProMaC | https://arxiv.org/abs/2408.15205v3 | S_{\alpha} | 0.767 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | GenSAM | https://arxiv.org/abs/2312.07374v3 | MAE | 0.113 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | GenSAM | https://arxiv.org/abs/2312.07374v3 | F_{\beta} | 0.659 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | GenSAM | https://arxiv.org/abs/2312.07374v3 | E_{\phi} | 0.775 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | CAMO | GenSAM | https://arxiv.org/abs/2312.07374v3 | S_{\alpha} | 0.719 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | RDVP-MSD | null | MAE | 0.038 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | RDVP-MSD | null | E_{\phi} | 0.877 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | RDVP-MSD | null | S_{\alpha} | 0.825 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | ProMaC | https://arxiv.org/abs/2408.15205v3 | MAE | 0.042 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | ProMaC | https://arxiv.org/abs/2408.15205v3 | F_{\beta} | 0.716 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | ProMaC | https://arxiv.org/abs/2408.15205v3 | E_{\phi} | 0.876 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | ProMaC | https://arxiv.org/abs/2408.15205v3 | S_{\alpha} | 0.805 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | GenSAM | https://arxiv.org/abs/2312.07374v3 | MAE | 0.067 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | GenSAM | https://arxiv.org/abs/2312.07374v3 | F_{\beta} | 0.681 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | GenSAM | https://arxiv.org/abs/2312.07374v3 | E_{\phi} | 0.838 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | COD10K | GenSAM | https://arxiv.org/abs/2312.07374v3 | S_{\alpha} | 0.775 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | ProMaC | https://arxiv.org/abs/2408.15205v3 | MAE | 0.044 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | ProMaC | https://arxiv.org/abs/2408.15205v3 | F_{\beta} | 0.79 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | ProMaC | https://arxiv.org/abs/2408.15205v3 | E_{\phi} | 0.899 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | ProMaC | https://arxiv.org/abs/2408.15205v3 | S_{\alpha} | 0.833 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | GenSAM | https://arxiv.org/abs/2312.07374v3 | MAE | 0.09 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | GenSAM | https://arxiv.org/abs/2312.07374v3 | F_{\beta} | 0.68 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | GenSAM | https://arxiv.org/abs/2312.07374v3 | E_{\phi} | 0.807 |
16k > Object Detection > Camouflaged Object Segmentation > Camouflaged Object Segmentation with a Single Task-generic Prompt | Chameleon | GenSAM | https://arxiv.org/abs/2312.07374v3 | S_{\alpha} | 0.764 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Electronic, Indoor, Kitchen, Furniture) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | MAP | 31.66 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Electronic, Indoor, Kitchen, Furniture) | ORE | https://arxiv.org/abs/2103.02603v2 | MAP | 26.66 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | WI | 0.0179 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | A-OSE | 4117 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | MAP | 36.75 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | Unknown Recall | 50.89 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE | https://arxiv.org/abs/2103.02603v2 | WI | 0.0081 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE | https://arxiv.org/abs/2103.02603v2 | A-OSE | 6634 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE | https://arxiv.org/abs/2103.02603v2 | MAP | 29.32 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Sports, Food) | ORE | https://arxiv.org/abs/2103.02603v2 | Unknown Recall | 14.79 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | A-OSE | 5212 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | WI | 0.0251 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | MAP | 46.19 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE (MDef-DETR) | https://arxiv.org/abs/2111.11430v6 | Unknown Recall | 49.54 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE | https://arxiv.org/abs/2103.02603v2 | A-OSE | 7772 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE | https://arxiv.org/abs/2103.02603v2 | WI | 0.0154 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE | https://arxiv.org/abs/2103.02603v2 | MAP | 38.98 |
16k > Object Detection > Open World Object Detection | COCO 2017 (Outdoor, Accessories, Appliance, Truck) | ORE | https://arxiv.org/abs/2103.02603v2 | Unknown Recall | 11.32 |
16k > Object Detection > Open World Object Detection | COCO-Mix | unsniffer | https://arxiv.org/abs/2303.13769v3 | unknown-AP | 0.150 |
16k > Object Detection > Open World Object Detection | COCO-Mix | unsniffer | https://arxiv.org/abs/2303.13769v3 | unknown F1 score | 0.287 |
16k > Object Detection > Open World Object Detection | COCO-OOD | unsniffer | https://arxiv.org/abs/2303.13769v3 | unknown-AP | 0.454 |
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