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 ⌀ |
|---|---|---|---|---|---|
Handwritten Mathmatical Expression Recognition | CROHME 2014 | NAMER | https://arxiv.org/abs/2407.11380v1 | ExpRate | 60.51 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | PosFormer | https://arxiv.org/abs/2407.07764v1 | ExpRate | 60.45 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | CoMER | https://arxiv.org/abs/2207.04410v2 | ExpRate | 58.38 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | CAN-ABM | https://arxiv.org/abs/2207.11463v1 | ExpRate | 57.26 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | CAN-DWAP | https://arxiv.org/abs/2207.11463v1 | ExpRate | 57.00 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | ABM | https://arxiv.org/abs/2112.03603v3 | ExpRate | 56.85 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | SAN | https://arxiv.org/abs/2203.01601v4 | ExpRate | 56.2 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | BTTR | https://arxiv.org/abs/2105.02412v3 | ExpRate | 53.96 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | TDv2 | https://ojs.aaai.org/index.php/AAAI/article/view/20172 | ExpRate | 53.62 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | DenseWAP-MSA | http://arxiv.org/abs/1801.03530v2 | ExpRate | 52.8 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | DenseWAP | http://arxiv.org/abs/1801.03530v2 | ExpRate | 50.1 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | TD | https://proceedings.icml.cc/static/paper_files/icml/2020/3307-Paper.pdf | ExpRate | 49.1 |
Handwritten Mathmatical Expression Recognition | CROHME 2014 | WAP | https://www.sciencedirect.com/science/article/pii/S0031320317302376 | ExpRate | 46.55 |
Concept-based Classification | aPY | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Task Accuracy (%) | 43.75 |
Concept-based Classification | aPY | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Concept Accuracy (%) | 71.19 |
Concept-based Classification | CUB-200-2011 | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Task Accuracy (%) | 79.68 |
Concept-based Classification | CUB-200-2011 | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Concept Accuracy (%) | 90.82 |
Concept-based Classification | CUB-200-2011 | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Task Accuracy (%) | 79.310 |
Concept-based Classification | CUB-200-2011 | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Concept Accuracy (%) | 96.580 |
Concept-based Classification | CelebA | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Task Accuracy (%) | 56.600 |
Concept-based Classification | CelebA | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Concept Accuracy (%) | 90.617 |
Concept-based Classification | CelebA | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Task Accuracy (%) | 42.10 |
Concept-based Classification | CelebA | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Concept Accuracy (%) | 86.44 |
Concept-based Classification | AwA2 | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Task Accuracy (%) | 95.965 |
Concept-based Classification | AwA2 | EQ-CBM (ResNet-34) | https://arxiv.org/abs/2409.14630v1 | Concept Accuracy (%) | 99.129 |
Concept-based Classification | AwA2 | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Task Accuracy (%) | 94.63 |
Concept-based Classification | AwA2 | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Concept Accuracy (%) | 93.68 |
Concept-based Classification | CheXpert | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Task Accuracy (%) | 89.25 |
Concept-based Classification | CheXpert | CGEM (ResNet-34) | https://iopscience.iop.org/article/10.1088/2632-2153/ad6ad2 | Concept Accuracy (%) | 63.52 |
Road Segmentation | ChesapeakeRSC | U-Net (ResNet-18) | https://arxiv.org/abs/2401.06762v1 | DWR | 46.5 |
Road Segmentation | ChesapeakeRSC | DeepLabV3+ (ResNet-18) | https://arxiv.org/abs/2401.06762v1 | DWR | 46.1 |
Road Segmentation | ChesapeakeRSC | U-Net (ResNet-50) | https://arxiv.org/abs/2401.06762v1 | DWR | 45.7 |
Road Segmentation | ChesapeakeRSC | FCN | https://arxiv.org/abs/2401.06762v1 | DWR | 10.7 |
Road Segmentation | Massachusetts Roads Dataset | RSM-SS | https://arxiv.org/abs/2404.02668v2 | IoU | 67.35 |
Road Segmentation | Massachusetts Roads Dataset | RSM-SS | https://arxiv.org/abs/2404.02668v2 | F1 | 80.49 |
Road Segmentation | Massachusetts Roads Dataset | SPIN Road Mapper (ours) | https://arxiv.org/abs/2109.07701v1 | IoU | 65.24 |
Road Segmentation | Massachusetts Roads Dataset | SPIN Road Mapper (ours) | https://arxiv.org/abs/2109.07701v1 | APLS | 72.49 |
Road Segmentation | DeepGlobe | SPIN Road Mapper (ours) | https://arxiv.org/abs/2109.07701v1 | APLS | 0.7414 |
Road Segmentation | DeepGlobe | SPIN Road Mapper (ours) | https://arxiv.org/abs/2109.07701v1 | IoU | 0.6702 |
Road Segmentation | DeepGlobe | D-LinkNet | https://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf | IoU | 0.6412 |
Road Segmentation | DeepGlobe | CoANet + PRN | https://arxiv.org/abs/2211.06560v3 | mIoU | 70.6 |
3D Shape Reconstruction from Videos > DeepFake Detection | วีเค..!! ชมคลิป ‘ไอซ์ ปรีชญา’ ลืมปิดถ่ายทอดสดขณะอาบน้ำ โด | Vdk | https://arxiv.org/abs/2402.04499v2 | 0-shot MRR | Gpg |
3D Shape Reconstruction from Videos > DeepFake Detection | DFDC | Cross Efficient Vision Transformer | https://arxiv.org/abs/2107.02612v2 | AUC | 0.951 |
3D Shape Reconstruction from Videos > DeepFake Detection | DFDC | Efficient Vision Transformer | https://arxiv.org/abs/2107.02612v2 | AUC | 0.919 |
3D Shape Reconstruction from Videos > DeepFake Detection | DFDC | EfficientNetB4 + EfficientNetB4ST + B4Att | https://arxiv.org/abs/2004.07676v1 | LogLoss | 0.4640 |
3D Shape Reconstruction from Videos > DeepFake Detection | ^(#$!@#$)(()))****** | Adi | https://arxiv.org/abs/2402.04499v2 | 0..5sec | I don't know |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | QAD-E | https://arxiv.org/abs/2309.05911v1 | AUC | 0.956 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | EfficientNetB4 + EfficientNetB4ST + B4Att + B4AttST | https://arxiv.org/abs/2004.07676v1 | AUC | 0.9444 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | MARLIN (ViT-L) | https://arxiv.org/abs/2211.06627v3 | AUC | 0.9377 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | MARLIN (ViT-B) | https://arxiv.org/abs/2211.06627v3 | AUC | 0.9305 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | MARLIN (ViT-S) | https://arxiv.org/abs/2211.06627v3 | AUC | 0.8863 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics++ | EfficientNetB4 + EfficientNetB4ST + B4AttST | https://arxiv.org/abs/2004.07676v1 | LogLoss | 0.3269 |
3D Shape Reconstruction from Videos > DeepFake Detection | COCOFake | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | Accuracy | 99.25 |
3D Shape Reconstruction from Videos > DeepFake Detection | COCOFake | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | AUC | 0.9986 |
3D Shape Reconstruction from Videos > DeepFake Detection | DFFD | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | Accuracy | 0.9895 |
3D Shape Reconstruction from Videos > DeepFake Detection | DFFD | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | AUC | 0.9994 |
3D Shape Reconstruction from Videos > DeepFake Detection | CIFAKE: Real and AI-Generated Synthetic Images | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | Validation Accuracy | 97.29 |
3D Shape Reconstruction from Videos > DeepFake Detection | CIFAKE: Real and AI-Generated Synthetic Images | FasterThanLies | https://arxiv.org/abs/2406.04932v1 | AUC | 99.65 |
3D Shape Reconstruction from Videos > DeepFake Detection | LAV-DF | BA-TFD | https://arxiv.org/abs/2204.06228v2 | AUC | 0.990 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | FACTOR | https://arxiv.org/abs/2311.01458v1 | ROC AUC | 97.4 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | FACTOR | https://arxiv.org/abs/2311.01458v1 | AP | 96.8 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | RealForensics | https://arxiv.org/abs/2201.07131v3 | ROC AUC | 97.1 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | RealForensics | https://arxiv.org/abs/2201.07131v3 | AP | 95.3 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AVAD | https://arxiv.org/abs/2301.01767v2 | ROC AUC | 94.5 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AVAD | https://arxiv.org/abs/2301.01767v2 | AP | 94.2 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | FTCN | https://arxiv.org/abs/2108.06693v1 | ROC AUC | 93.1 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | FTCN | https://arxiv.org/abs/2108.06693v1 | AP | 92.3 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | LipForensics | https://arxiv.org/abs/2012.07657v3 | ROC AUC | 91.1 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | LipForensics | https://arxiv.org/abs/2012.07657v3 | AP | 89.4 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AD DFD | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Joint_Audio-Visual_Deepfake_Detection_ICCV_2021_paper.html | ROC AUC | 88.1 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AD DFD | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Joint_Audio-Visual_Deepfake_Detection_ICCV_2021_paper.html | AP | 88.8 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | Xception | https://arxiv.org/abs/1901.08971v3 | ROC AUC | 85.3 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | Xception | https://arxiv.org/abs/1901.08971v3 | AP | 84.8 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AVBYOL | https://arxiv.org/abs/2201.07131v3 | ROC AUC | 59.2 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AVBYOL | https://arxiv.org/abs/2201.07131v3 | AP | 73.9 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | VQGAN | https://arxiv.org/abs/2012.09841v3 | ROC AUC | 51.8 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | VQGAN | https://arxiv.org/abs/2012.09841v3 | AP | 55.0 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AV-Lip-Sync+ | https://arxiv.org/abs/2311.02733v1 | Accuracy (%) | 99.29 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | Avtenet | https://arxiv.org/abs/2310.13103v1 | Accuracy (%) | 98.57 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | AV-Lip-Sync Model | https://ieeexplore.ieee.org/document/9980296 | Accuracy (%) | 94 |
3D Shape Reconstruction from Videos > DeepFake Detection | FakeAVCeleb | Multimodal Ensemble Model | https://ieeexplore.ieee.org/document/9980296 | Accuracy (%) | 89 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | DF | 96.36 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | FS | 90.29 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | FSF | 86.86 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | NT | 80.67 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | Real | 52.4 |
3D Shape Reconstruction from Videos > DeepFake Detection | FaceForensics | XceptionNet | https://arxiv.org/abs/1901.08971v3 | Total Accuracy | 70.1 |
3D Shape Reconstruction from Videos > DeepFake Detection | 1 | STYLE | https://arxiv.org/abs/2206.09379v2 | 0L | 99 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | FakeOrReal | rawnet_lite.pt | https://arxiv.org/abs/2504.20923v2 | EER | 0.25 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR-Mamba | https://arxiv.org/abs/2411.10027v2 | 21LA EER | 0.93 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR-Mamba | https://arxiv.org/abs/2411.10027v2 | 21DF EER | 1.88 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR+AASIST | https://arxiv.org/abs/2202.12233v2 | 21LA EER | 1.0 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR+AASIST | https://arxiv.org/abs/2202.12233v2 | 21DF EER | 3.69 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR+SLS | https://openreview.net/pdf?id=acJMIXJg2u | 21LA EER | 2.86 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | XLSR+SLS | https://openreview.net/pdf?id=acJMIXJg2u | 21DF EER | 1.96 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | TCM-Add | https://arxiv.org/abs/2406.17376v1 | 21LA EER | 2.99 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | TCM-Add | https://arxiv.org/abs/2406.17376v1 | 21DF EER | 2.14 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | BTS-E | https://ieeexplore.ieee.org/abstract/document/10095927 | 21LA EER | 8.75 |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | BTS-E | https://ieeexplore.ieee.org/abstract/document/10095927 | 21DF EER | / |
3D Shape Reconstruction from Videos > DeepFake Detection > Audio Deepfake Detection | ASVspoof 2021 | RawGAT-ST | https://arxiv.org/abs/2107.12710v2 | 21LA EER | 10.25 |
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