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 Point Cloud Reconstruction > 3D Point Cloud Classification > Zero-Shot Transfer 3D Point Cloud Classification | ModelNet10 | CLIP2Point | https://arxiv.org/abs/2210.01055v3 | Accuracy (%) | 66.63 |
3D Point Cloud Reconstruction > 3D Point Cloud Classification > Zero-Shot Transfer 3D Point Cloud Classification | ModelNet10 | PointCLIP | https://arxiv.org/abs/2112.02413v1 | Accuracy (%) | 30.23 |
Generative 3D Object Classification | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Objaverse (I) | 60.00 |
Generative 3D Object Classification | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Objaverse (Average) | 60.25 |
Generative 3D Object Classification | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Objaverse (C) | 60.50 |
Generative 3D Object Classification | Objaverse | ShapeLLM-7B | https://arxiv.org/abs/2402.17766v3 | Objaverse (Average) | 54.50 |
Generative 3D Object Classification | Objaverse | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (I) | 56.50 |
Generative 3D Object Classification | Objaverse | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (Average) | 54.00 |
Generative 3D Object Classification | Objaverse | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (C) | 51.50 |
Generative 3D Object Classification | Objaverse | ShapeLLM-13B | https://arxiv.org/abs/2402.17766v3 | Objaverse (Average) | 54.00 |
Generative 3D Object Classification | Objaverse | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (I) | 55.00 |
Generative 3D Object Classification | Objaverse | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (Average) | 53.00 |
Generative 3D Object Classification | Objaverse | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | Objaverse (C) | 51.00 |
Generative 3D Object Classification | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Objaverse (I) | 49.00 |
Generative 3D Object Classification | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Objaverse (Average) | 45.25 |
Generative 3D Object Classification | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Objaverse (C) | 41.50 |
Generative 3D Object Classification | Objaverse | Point-Bind LLM | https://arxiv.org/abs/2309.00615v1 | Objaverse (I) | 6.00 |
Generative 3D Object Classification | Objaverse | Point-Bind LLM | https://arxiv.org/abs/2309.00615v1 | Objaverse (Average) | 5.25 |
Generative 3D Object Classification | Objaverse | Point-Bind LLM | https://arxiv.org/abs/2309.00615v1 | Objaverse (C) | 4.50 |
Generative 3D Object Classification | ModelNet40 | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | ModelNet40 (Average) | 60.86 |
Generative 3D Object Classification | ModelNet40 | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | ModelNet40 (I) | 61.75 |
Generative 3D Object Classification | ModelNet40 | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | ModelNet40 (C) | 59.97 |
Generative 3D Object Classification | ModelNet40 | ShapeLLM-7B | https://arxiv.org/abs/2402.17766v3 | ModelNet40 (Average) | 53.08 |
Generative 3D Object Classification | ModelNet40 | ShapeLLM-13B | https://arxiv.org/abs/2402.17766v3 | ModelNet40 (Average) | 52.96 |
Generative 3D Object Classification | ModelNet40 | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (Average) | 52.78 |
Generative 3D Object Classification | ModelNet40 | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (I) | 53.00 |
Generative 3D Object Classification | ModelNet40 | PointLLM-13B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (C) | 52.55 |
Generative 3D Object Classification | ModelNet40 | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (Average) | 52.63 |
Generative 3D Object Classification | ModelNet40 | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (I) | 53.44 |
Generative 3D Object Classification | ModelNet40 | PointLLM-7B v1.2 | https://arxiv.org/abs/2308.16911v3 | ModelNet40 (C) | 51.82 |
Generative 3D Object Classification | ModelNet40 | Point-Bind LLM | https://arxiv.org/abs/2309.00615v1 | ModelNet40 (Average) | 45.81 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | AUC | 0.9874 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | F1 score | 0.8156 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | mIoU | 0.8308 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Acc | 0.9710 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Sensitivity | 0.8361 |
Retinal Vessel Segmentation | HRF | FSG-Net | https://arxiv.org/abs/2501.18921v1 | MCC | 0.8012 |
Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | AUC | 0.9845 |
Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | MCC | 0.8012 |
Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | 1:1 Accuracy | 0.9723 |
Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | DSC | 0.8289 |
Retinal Vessel Segmentation | HRF | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | Average IOU | 0.7089 |
Retinal Vessel Segmentation | HRF | VGN | http://arxiv.org/abs/1806.02279v1 | AUC | 0.9838 |
Retinal Vessel Segmentation | HRF | VGN | http://arxiv.org/abs/1806.02279v1 | F1 score | 0.8151 |
Retinal Vessel Segmentation | HRF | U-Net ASPP | https://arxiv.org/abs/2307.14179v1 | mIoU | 0.8966 |
Retinal Vessel Segmentation | ROSE-2 | OCTAve: OCTA-Net | https://arxiv.org/abs/2207.12238v1 | Dice Score | 71.18 |
Retinal Vessel Segmentation | ROSE-2 | OCTA-Net | https://arxiv.org/abs/2007.05201v2 | Dice Score | 70.77 |
Retinal Vessel Segmentation | ROSE-2 | CE-Net | http://arxiv.org/abs/1903.02740v1 | Dice Score | 70.66 |
Retinal Vessel Segmentation | ROSE-2 | ResU-Net | http://arxiv.org/abs/1711.10684v1 | Dice Score | 67.25 |
Retinal Vessel Segmentation | ROSE-2 | U-Net | http://arxiv.org/abs/1505.04597v1 | Dice Score | 65.64 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | F1 score | 0.8101 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | AUC | 0.9937 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | mIOU | 0.8268 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Sensitivity | 0.8599 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | Acc | 0.9751 |
Retinal Vessel Segmentation | CHASE_DB1 | FSG-Net | https://arxiv.org/abs/2501.18921v1 | MCC | 0.7989 |
Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | F1 score | 0.8271 |
Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | AUC | 0.9920 |
Retinal Vessel Segmentation | CHASE_DB1 | Study Group Learning | https://arxiv.org/abs/2103.03451v1 | Sensitivity | 0.8690 |
Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | F1 score | 0.8957 |
Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | AUC | 0.9914 |
Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | mIOU | 0.9705 |
Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | https://arxiv.org/abs/2101.00535v2 | Sensitivity | 0.8199 |
Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | F1 score | 0.8151 |
Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | AUC | 0.9913 |
Retinal Vessel Segmentation | CHASE_DB1 | FR-UNet | https://ieeexplore.ieee.org/abstract/document/9815506 | Sensitivity | 0.8798 |
Retinal Vessel Segmentation | CHASE_DB1 | SA-UNet | https://arxiv.org/abs/2004.03696v3 | F1 score | 0.8153 |
Retinal Vessel Segmentation | CHASE_DB1 | SA-UNet | https://arxiv.org/abs/2004.03696v3 | AUC | 0.9905 |
Retinal Vessel Segmentation | CHASE_DB1 | IterNet | https://arxiv.org/abs/1912.05763v1 | F1 score | 0.8073 |
Retinal Vessel Segmentation | CHASE_DB1 | IterNet | https://arxiv.org/abs/1912.05763v1 | AUC | 0.9851 |
Retinal Vessel Segmentation | CHASE_DB1 | LadderNet | https://arxiv.org/abs/1810.07810v4 | F1 score | 0.8031 |
Retinal Vessel Segmentation | CHASE_DB1 | LadderNet | https://arxiv.org/abs/1810.07810v4 | AUC | 0.9839 |
Retinal Vessel Segmentation | CHASE_DB1 | VGN | http://arxiv.org/abs/1806.02279v1 | F1 score | 0.8034 |
Retinal Vessel Segmentation | CHASE_DB1 | VGN | http://arxiv.org/abs/1806.02279v1 | AUC | 0.9830 |
Retinal Vessel Segmentation | CHASE_DB1 | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | AUC | 0.9823 |
Retinal Vessel Segmentation | CHASE_DB1 | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | MCC | 0.7892 |
Retinal Vessel Segmentation | CHASE_DB1 | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | 1:1 Accuracy | 0.9712 |
Retinal Vessel Segmentation | CHASE_DB1 | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | DSC | 0.8156 |
Retinal Vessel Segmentation | CHASE_DB1 | DEFFA-Unet | https://arxiv.org/abs/2506.02312v1 | Average IOU | 0.6891 |
Retinal Vessel Segmentation | CHASE_DB1 | R2U-Net | http://arxiv.org/abs/1802.06955v5 | F1 score | 0.7928 |
Retinal Vessel Segmentation | CHASE_DB1 | R2U-Net | http://arxiv.org/abs/1802.06955v5 | AUC | 0.9815 |
Retinal Vessel Segmentation | CHASE_DB1 | DUNet | http://arxiv.org/abs/1811.01206v1 | F1 score | 0.7883 |
Retinal Vessel Segmentation | CHASE_DB1 | DUNet | http://arxiv.org/abs/1811.01206v1 | AUC | 0.9804 |
Retinal Vessel Segmentation | CHASE_DB1 | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | F1 score | 0.7800 |
Retinal Vessel Segmentation | CHASE_DB1 | Residual U-Net | http://arxiv.org/abs/1711.10684v1 | AUC | 0.9779 |
Retinal Vessel Segmentation | CHASE_DB1 | U-Net | http://arxiv.org/abs/1505.04597v1 | AUC | 0.9772 |
Retinal Vessel Segmentation | CHASE_DB1 | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8267 |
Retinal Vessel Segmentation | CHASE_DB1 | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIOU | 0.7050 |
Retinal Vessel Segmentation | CHASE_DB1 | MERIT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Sensitivity | 0.8493 |
Retinal Vessel Segmentation | CHASE_DB1 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | F1 score | 0.8251 |
Retinal Vessel Segmentation | CHASE_DB1 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIOU | 0.7024 |
Retinal Vessel Segmentation | CHASE_DB1 | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | Sensitivity | 0.8584 |
Retinal Vessel Segmentation | CHASE_DB1 | U-Net ASPP | https://arxiv.org/abs/2307.14179v1 | mIOU | 0.8959 |
Retinal Vessel Segmentation | UZLF | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 83.2 |
Retinal Vessel Segmentation | UZLF | Junior Ophtalmologist | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 82.6 |
Retinal Vessel Segmentation | UZLF | VascX | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 80.6 |
Retinal Vessel Segmentation | UZLF | Automorph | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 74.0 |
Retinal Vessel Segmentation | UZLF | Little W-Net | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 60.9 |
Retinal Vessel Segmentation | ROSE-1 SVC-DVC | OCTAve: OCTA-Net | https://arxiv.org/abs/2207.12238v1 | Dice Score | 81.42 |
Retinal Vessel Segmentation | ROSE-1 SVC-DVC | OCTA-Net | https://arxiv.org/abs/2007.05201v2 | Dice Score | 75.76 |
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