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 ⌀ |
|---|---|---|---|---|---|
Deblurring | HIDE (trained on GOPRO) | DeblurDiNAT-L | https://arxiv.org/abs/2403.13163v5 | Params (M) | 16.1 |
Deblurring | HIDE (trained on GOPRO) | Restormer | https://arxiv.org/abs/2111.09881v2 | PSNR (sRGB) | 31.22 |
Deblurring | HIDE (trained on GOPRO) | Restormer | https://arxiv.org/abs/2111.09881v2 | SSIM (sRGB) | 0.942 |
Deblurring | HIDE (trained on GOPRO) | Restormer | https://arxiv.org/abs/2111.09881v2 | Params (M) | 26.13 |
Deblurring | HIDE (trained on GOPRO) | MPRNet-TLC | https://arxiv.org/abs/2112.04491v4 | PSNR (sRGB) | 31.19 |
Deblurring | HIDE (trained on GOPRO) | MPRNet-TLC | https://arxiv.org/abs/2112.04491v4 | SSIM (sRGB) | 0.942 |
Deblurring | HIDE (trained on GOPRO) | MPRNet-TLC | https://arxiv.org/abs/2112.04491v4 | Params (M) | 20.1 |
Deblurring | HIDE (trained on GOPRO) | Stripformer | https://arxiv.org/abs/2204.04627v2 | PSNR (sRGB) | 31.03 |
Deblurring | HIDE (trained on GOPRO) | Stripformer | https://arxiv.org/abs/2204.04627v2 | SSIM (sRGB) | 0.94 |
Deblurring | HIDE (trained on GOPRO) | MPRNet | https://arxiv.org/abs/2102.02808v2 | PSNR (sRGB) | 30.96 |
Deblurring | HIDE (trained on GOPRO) | MPRNet | https://arxiv.org/abs/2102.02808v2 | SSIM (sRGB) | 0.939 |
Deblurring | HIDE (trained on GOPRO) | MPRNet | https://arxiv.org/abs/2102.02808v2 | Params (M) | 20.1 |
Deblurring | HIDE (trained on GOPRO) | Uformer-B | https://arxiv.org/abs/2106.03106v2 | PSNR (sRGB) | 30.83 |
Deblurring | HIDE (trained on GOPRO) | Uformer-B | https://arxiv.org/abs/2106.03106v2 | SSIM (sRGB) | 0.952 |
Deblurring | HIDE (trained on GOPRO) | Uformer-B | https://arxiv.org/abs/2106.03106v2 | Params (M) | 50.88 |
Deblurring | HIDE (trained on GOPRO) | BANet | https://arxiv.org/abs/2101.07518v4 | PSNR (sRGB) | 30.16 |
Deblurring | HIDE (trained on GOPRO) | BANet | https://arxiv.org/abs/2101.07518v4 | SSIM (sRGB) | 0.93 |
Deblurring | HIDE (trained on GOPRO) | Suin et al | null | PSNR (sRGB) | 29.98 |
Deblurring | HIDE (trained on GOPRO) | Suin et al | null | SSIM (sRGB) | 0.930 |
Deblurring | HIDE (trained on GOPRO) | MT-RNN | https://arxiv.org/abs/1911.07410v1 | PSNR (sRGB) | 29.15 |
Deblurring | HIDE (trained on GOPRO) | MT-RNN | https://arxiv.org/abs/1911.07410v1 | SSIM (sRGB) | 0.918 |
Deblurring | HIDE (trained on GOPRO) | MT-RNN | https://arxiv.org/abs/1911.07410v1 | Params (M) | 2.6 |
Deblurring | HIDE (trained on GOPRO) | Gao et al | null | PSNR (sRGB) | 29.11 |
Deblurring | HIDE (trained on GOPRO) | Gao et al | null | SSIM (sRGB) | 0.913 |
Deblurring | HIDE (trained on GOPRO) | DMPHN | http://arxiv.org/abs/1904.03468v1 | PSNR (sRGB) | 29.09 |
Deblurring | HIDE (trained on GOPRO) | DMPHN | http://arxiv.org/abs/1904.03468v1 | SSIM (sRGB) | 0.924 |
Deblurring | HIDE (trained on GOPRO) | DMPHN | http://arxiv.org/abs/1904.03468v1 | Params (M) | 7.23 |
Deblurring | HIDE (trained on GOPRO) | DBGAN | https://arxiv.org/abs/2004.01860v2 | PSNR (sRGB) | 28.94 |
Deblurring | HIDE (trained on GOPRO) | DBGAN | https://arxiv.org/abs/2004.01860v2 | SSIM (sRGB) | 0.915 |
Deblurring | HIDE (trained on GOPRO) | SRN | http://arxiv.org/abs/1802.01770v1 | PSNR (sRGB) | 28.36 |
Deblurring | HIDE (trained on GOPRO) | SRN | http://arxiv.org/abs/1802.01770v1 | SSIM (sRGB) | 0.915 |
Deblurring | HIDE (trained on GOPRO) | SRN | http://arxiv.org/abs/1802.01770v1 | Params (M) | 8.06 |
Deblurring | HIDE (trained on GOPRO) | Nah et al | http://arxiv.org/abs/1612.02177v2 | PSNR (sRGB) | 25.73 |
Deblurring | RSBlur | MLWNet | https://arxiv.org/abs/2401.00027v2 | Average PSNR | 34.94 |
Deblurring | RSBlur | MLWNet | https://arxiv.org/abs/2401.00027v2 | SSIM | 0.880 |
Deblurring | RSBlur | SegDeblur | https://arxiv.org/abs/2404.12168v1 | Average PSNR | 34.63 |
Deblurring | RSBlur | SFNet | https://openreview.net/forum?id=tyZ1ChGZIKO | Average PSNR | 34.35 |
Deblurring | RSBlur | FSNet | https://ieeexplore.ieee.org/document/10310164 | Average PSNR | 34.31 |
Deblurring | RSBlur | IRNext | https://openreview.net/forum?id=MZkbgahv4a | Average PSNR | 34.08 |
Deblurring | RSBlur | ConvIR | https://ieeexplore.ieee.org/abstract/document/10571568 | Average PSNR | 34.06 |
Deblurring | RSBlur | Uformer-B | https://arxiv.org/abs/2106.03106v2 | Average PSNR | 33.98 |
Deblurring | RSBlur | Restormer | https://arxiv.org/abs/2111.09881v2 | Average PSNR | 33.69 |
Deblurring | RSBlur | MPRNet | https://arxiv.org/abs/2102.02808v2 | Average PSNR | 33.61 |
Deblurring | RSBlur | MIMO-UNet+ | https://arxiv.org/abs/2108.05054v2 | Average PSNR | 33.37 |
Deblurring | RSBlur | MIMO-UNet | https://arxiv.org/abs/2108.05054v2 | Average PSNR | 32.73 |
Deblurring | RSBlur | SRN-Deblur | http://arxiv.org/abs/1802.01770v1 | Average PSNR | 32.53 |
Deblurring | RSBlur (trained on synthetic) | MIMO-UNet + Realistic blur | https://arxiv.org/abs/2202.08771v3 | Average PSNR | 32.08 |
Deblurring | RSBlur (trained on synthetic) | SRN-Deblur + Realistic blur | https://arxiv.org/abs/2202.08771v3 | Average PSNR | 32.06 |
Structured Prediction | MNIST | CVAE | http://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models | Negative CLL | 71.8 |
Trajectory Prediction | ETH | Social-Implicit | https://arxiv.org/abs/2203.03057v2 | Avg AMD/AMV 8/12 | 0.90 |
Trajectory Prediction | ETH | Trajectron++ | https://arxiv.org/abs/2001.03093v5 | Avg AMD/AMV 8/12 | 1.01 |
Trajectory Prediction | ETH | Social-STGCNN | https://arxiv.org/abs/2002.11927v3 | Avg AMD/AMV 8/12 | 1.26 |
Trajectory Prediction | ETH | Social-GAN | http://arxiv.org/abs/1803.10892v1 | Avg AMD/AMV 8/12 | 1.42 |
Trajectory Prediction | ETH | ExpertTraj | http://openaccess.thecvf.com//content/ICCV2021/html/Zhao_Where_Are_You_Heading_Dynamic_Trajectory_Prediction_With_Expert_Goal_ICCV_2021_paper.html | Avg AMD/AMV 8/12 | 19.20 |
Trajectory Prediction | Hotel BIWI Walking Pedestrians dataset | Social Ways | http://arxiv.org/abs/1904.09507v2 | ADE-8/12 | 0.39 |
Trajectory Prediction | JAAD | SGNet | https://arxiv.org/abs/2103.14107v3 | MSE(0.5) | 82 |
Trajectory Prediction | JAAD | SGNet | https://arxiv.org/abs/2103.14107v3 | MSE(1.0) | 328 |
Trajectory Prediction | JAAD | SGNet | https://arxiv.org/abs/2103.14107v3 | MSE(1.5) | 1049 |
Trajectory Prediction | JAAD | SGNet | https://arxiv.org/abs/2103.14107v3 | C_MSE(1.5) | 996 |
Trajectory Prediction | JAAD | SGNet | https://arxiv.org/abs/2103.14107v3 | CF_MSE(1.5) | 4076 |
Trajectory Prediction | JAAD | BiTrap-D | https://arxiv.org/abs/2007.14558v2 | MSE(0.5) | 93 |
Trajectory Prediction | JAAD | BiTrap-D | https://arxiv.org/abs/2007.14558v2 | MSE(1.0) | 378 |
Trajectory Prediction | JAAD | BiTrap-D | https://arxiv.org/abs/2007.14558v2 | MSE(1.5) | 1206 |
Trajectory Prediction | JAAD | BiTrap-D | https://arxiv.org/abs/2007.14558v2 | C_MSE(1.5) | 1105 |
Trajectory Prediction | JAAD | BiTrap-D | https://arxiv.org/abs/2007.14558v2 | CF_MSE(1.5) | 4565 |
Trajectory Prediction | JAAD | PIE_traj | http://openaccess.thecvf.com/content_ICCV_2019/html/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.html | MSE(0.5) | 110 |
Trajectory Prediction | JAAD | PIE_traj | http://openaccess.thecvf.com/content_ICCV_2019/html/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.html | MSE(1.0) | 399 |
Trajectory Prediction | JAAD | PIE_traj | http://openaccess.thecvf.com/content_ICCV_2019/html/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.html | MSE(1.5) | 1280 |
Trajectory Prediction | JAAD | PIE_traj | http://openaccess.thecvf.com/content_ICCV_2019/html/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.html | C_MSE(1.5) | 1183 |
Trajectory Prediction | JAAD | PIE_traj | http://openaccess.thecvf.com/content_ICCV_2019/html/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.html | CF_MSE(1.5) | 4780 |
Trajectory Prediction | JAAD | FOL-X | https://arxiv.org/abs/1903.00618v4 | MSE(0.5) | 147 |
Trajectory Prediction | JAAD | FOL-X | https://arxiv.org/abs/1903.00618v4 | MSE(1.0) | 484 |
Trajectory Prediction | JAAD | FOL-X | https://arxiv.org/abs/1903.00618v4 | MSE(1.5) | 1374 |
Trajectory Prediction | JAAD | FOL-X | https://arxiv.org/abs/1903.00618v4 | C_MSE(1.5) | 1290 |
Trajectory Prediction | JAAD | FOL-X | https://arxiv.org/abs/1903.00618v4 | CF_MSE(1.5) | 4924 |
Trajectory Prediction | JAAD | Bayesian-LSTM | http://arxiv.org/abs/1711.09026v2 | MSE(0.5) | 159 |
Trajectory Prediction | JAAD | Bayesian-LSTM | http://arxiv.org/abs/1711.09026v2 | MSE(1.0) | 539 |
Trajectory Prediction | JAAD | Bayesian-LSTM | http://arxiv.org/abs/1711.09026v2 | MSE(1.5) | 1535 |
Trajectory Prediction | JAAD | Bayesian-LSTM | http://arxiv.org/abs/1711.09026v2 | C_MSE(1.5) | 1447 |
Trajectory Prediction | JAAD | Bayesian-LSTM | http://arxiv.org/abs/1711.09026v2 | CF_MSE(1.5) | 5615 |
Trajectory Prediction | Argoverse2 | HeteroGCN | https://arxiv.org/abs/2303.04364v1 | minADE (K=6) | 0.69 |
Trajectory Prediction | Argoverse2 | HeteroGCN | https://arxiv.org/abs/2303.04364v1 | minFDE (K=6) | 1.34 |
Trajectory Prediction | Argoverse2 | HeteroGCN | https://arxiv.org/abs/2303.04364v1 | MR (K=6) | 0.18 |
Trajectory Prediction | Argoverse2 | HeteroGCN | https://arxiv.org/abs/2303.04364v1 | brier-minFDE (K=6) | 1.90 |
Trajectory Prediction | STATS SportVu NBA [ATK] | DAG-Net | https://arxiv.org/abs/2005.12661v2 | ADE | 9.18 |
Trajectory Prediction | STATS SportVu NBA [ATK] | DAG-Net | https://arxiv.org/abs/2005.12661v2 | FDE | 13.54 |
Trajectory Prediction | Apolloscape Trajectory | Trafficpredict | http://arxiv.org/abs/1811.02146v5 | ADE | 8.5881 |
Trajectory Prediction | HEV-I | SGNet | https://arxiv.org/abs/2103.14107v3 | ADE(0.5) | 6.28 |
Trajectory Prediction | HEV-I | SGNet | https://arxiv.org/abs/2103.14107v3 | ADE(1.0) | 11.35 |
Trajectory Prediction | HEV-I | SGNet | https://arxiv.org/abs/2103.14107v3 | ADE(1.5) | 18.27 |
Trajectory Prediction | HEV-I | SGNet | https://arxiv.org/abs/2103.14107v3 | FDE(1.5) | 39.86 |
Trajectory Prediction | HEV-I | SGNet | https://arxiv.org/abs/2103.14107v3 | FIOU(1.5) | 0.63 |
Trajectory Prediction | HEV-I | FOL-X | https://arxiv.org/abs/1903.00618v4 | ADE(0.5) | 6.70 |
Trajectory Prediction | HEV-I | FOL-X | https://arxiv.org/abs/1903.00618v4 | ADE(1.0) | 12.60 |
Trajectory Prediction | HEV-I | FOL-X | https://arxiv.org/abs/1903.00618v4 | ADE(1.5) | 20.40 |
Trajectory Prediction | HEV-I | FOL-X | https://arxiv.org/abs/1903.00618v4 | FDE(1.5) | 44.10 |
Trajectory Prediction | HEV-I | FOL-X | https://arxiv.org/abs/1903.00618v4 | FIOU(1.5) | 0.61 |
Trajectory Prediction | ApolloScape | SpectralCows | https://arxiv.org/abs/1912.01118v1 | ADE | 0.005 |
Trajectory Prediction | ApolloScape | rule-based | https://arxiv.org/abs/2010.12007v2 | FDE | 5.992 |
Trajectory Prediction | NGSIM | Pishgu | https://arxiv.org/abs/2210.08057v3 | ADE | 0.88 |
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