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 Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma50 | Big-CDLNet | https://arxiv.org/abs/2103.04779v1 | PSNR | 26.35 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma50 | Index Network | https://arxiv.org/abs/1908.09895v2 | PSNR | 26.34 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma50 | FFDNet | http://arxiv.org/abs/1710.04026v2 | PSNR | 26.29 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma50 | Deep CNN Denoiser | http://arxiv.org/abs/1704.03264v1 | PSNR | 26.19 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma50 | BUIFD75 (blind) | https://arxiv.org/abs/1907.03029v2 | PSNR | 25.1 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD68 sigma60 | BUIFD75 (blind) | https://arxiv.org/abs/1907.03029v2 | PSNR | 24.05 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma10 | RC-Net | https://arxiv.org/abs/1910.08853v1 | PSNR | 36.36 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma10 | RED30 | http://arxiv.org/abs/1606.08921v3 | PSNR | 33.63 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma10 | RED30 | http://arxiv.org/abs/1606.08921v3 | SSIM | 0.9319 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma70 | RC-Net | https://arxiv.org/abs/1910.08853v1 | PSNR | 31.17 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma70 | NLRN-MV | http://arxiv.org/abs/1806.02919v2 | PSNR | 24.62 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma70 | RED30 | http://arxiv.org/abs/1606.08921v3 | PSNR | 24.37 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma70 | RED30 | http://arxiv.org/abs/1606.08921v3 | SSIM | 0.6551 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma30 | RC-Net | https://arxiv.org/abs/1910.08853v1 | PSNR | 33.57 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma30 | NLRN-MV | http://arxiv.org/abs/1806.02919v2 | PSNR | 28.2 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma30 | RED30 | http://arxiv.org/abs/1606.08921v3 | PSNR | 27.95 |
3D Architecture > Denoising > Grayscale Image Denoising | BSD200 sigma30 | RED30 | http://arxiv.org/abs/1606.08921v3 | SSIM | 0.8019 |
3D Architecture > Denoising > Grayscale Image Denoising | Set12 sigma70 | N3Net | http://arxiv.org/abs/1810.12575v1 | PSNR | 25.9 |
3D Architecture > Denoising > Grayscale Image Denoising | Kodak24 sigma50 | Residual Dense Network + | https://arxiv.org/abs/1812.10477v2 | PSNR | 27.88 |
3D Architecture > Denoising > Grayscale Image Denoising | Clip300 sigma60 | FFDNet-Clip | http://arxiv.org/abs/1710.04026v2 | PSNR | 25.51 |
3D Architecture > Denoising > Grayscale Image Denoising | Urban100 sigma30 | Residual Dense Network + | https://arxiv.org/abs/1812.10477v2 | PSNR | 30.08 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 30% | RTF-Net | https://arxiv.org/abs/2502.09000v1 | PSNR | 44.56 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 30% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 40.90 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 30% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 39.83 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 50% | RTF-Net | https://arxiv.org/abs/2502.09000v1 | PSNR | 38.03 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 50% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 37.28 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 50% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 35.92 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 50% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 34.35 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 50% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 32.27 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 50% | DeepBossting | http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.html | PSNR | 19.50 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 70% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 31.56 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 70% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 30.49 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 70% | DeepBoosting | http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.html | PSNR | 15.74 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 70% | RTF-Net | https://arxiv.org/abs/2502.09000v1 | PSNR | 34.96 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 70% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 32.4 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | BSD300 Noise Level 70% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 31.42 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 30% | CNN (Median Layers) | https://arxiv.org/abs/1908.06452v1 | PSNR | 36.39 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 30% | Noise2Noise | http://arxiv.org/abs/1803.04189v3 | PSNR | 34.95 |
3D Architecture > Denoising > Salt-And-Pepper Noise Removal | Kodak24 Noise Level 30% | DeepBoosting | http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Chen_Deep_Boosting_for_ECCV_2018_paper.html | PSNR | 21.69 |
Motion Captioning | HumanML3D | ST-MLP | https://arxiv.org/abs/2310.07324v2 | BLEU-4 | 25.0 |
Motion Captioning | HumanML3D | ST-MLP | https://arxiv.org/abs/2310.07324v2 | BERTScore | 40.3 |
Motion Captioning | HumanML3D | MLP+GRU | https://arxiv.org/abs/2310.10594v2 | BLEU-4 | 23.4 |
Motion Captioning | HumanML3D | MLP+GRU | https://arxiv.org/abs/2310.10594v2 | BERTScore | 37.2 |
Motion Captioning | HumanML3D | TM2T | https://arxiv.org/abs/2207.01696v2 | BLEU-4 | 22.3 |
Motion Captioning | HumanML3D | TM2T | https://arxiv.org/abs/2207.01696v2 | BERTScore | 37.8 |
Motion Captioning | HumanML3D | MotionGPT | https://arxiv.org/abs/2306.14795v2 | BLEU-4 | 12.47 |
Motion Captioning | HumanML3D | MotionGPT | https://arxiv.org/abs/2306.14795v2 | BERTScore | 32.4 |
Motion Captioning | KIT Motion-Language | MLP+GRU | https://arxiv.org/abs/2310.10594v2 | BLEU-4 | 25.4 |
Motion Captioning | KIT Motion-Language | MLP+GRU | https://arxiv.org/abs/2310.10594v2 | BERTScore | 42.1 |
Motion Captioning | KIT Motion-Language | ST-MLP | https://arxiv.org/abs/2310.07324v2 | BLEU-4 | 24.4 |
Motion Captioning | KIT Motion-Language | ST-MLP | https://arxiv.org/abs/2310.07324v2 | BERTScore | 41.2 |
Motion Captioning | KIT Motion-Language | TM2T | https://arxiv.org/abs/2207.01696v2 | BLEU-4 | 18.4 |
Motion Captioning | KIT Motion-Language | TM2T | https://arxiv.org/abs/2207.01696v2 | BERTScore | 23.0 |
Point Clouds | DTU | Vis-MVSNet | https://arxiv.org/abs/2008.07928v2 | Overall | 0.365 |
Point Clouds | Tanks and Temples | MVSFormer++ | https://arxiv.org/abs/2401.11673v1 | Mean F1 (Intermediate) | 67.03 |
Point Clouds | Tanks and Temples | MVSFormer++ | https://arxiv.org/abs/2401.11673v1 | Mean F1 (Advanced) | 41.70 |
Point Clouds | Tanks and Temples | GeoMVSNet | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | Mean F1 (Intermediate) | 65.89 |
Point Clouds | Tanks and Temples | GeoMVSNet | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_GeoMVSNet_Learning_Multi-View_Stereo_With_Geometry_Perception_CVPR_2023_paper.html | Mean F1 (Advanced) | 41.52 |
Point Clouds | Tanks and Temples | MVSFormer | https://arxiv.org/abs/2208.02541v3 | Mean F1 (Intermediate) | 66.37 |
Point Clouds | Tanks and Temples | MVSFormer | https://arxiv.org/abs/2208.02541v3 | Mean F1 (Advanced) | 40.87 |
Point Clouds | Tanks and Temples | ET-MVSNet | https://arxiv.org/abs/2309.17218v1 | Mean F1 (Intermediate) | 65.49 |
Point Clouds | Tanks and Temples | ET-MVSNet | https://arxiv.org/abs/2309.17218v1 | Mean F1 (Advanced) | 40.41 |
Point Clouds | Tanks and Temples | DPE-MVS | https://arxiv.org/abs/2412.20328v1 | Mean F1 (Intermediate) | 63.98 |
Point Clouds | Tanks and Temples | DPE-MVS | https://arxiv.org/abs/2412.20328v1 | Mean F1 (Advanced) | 40.20 |
Point Clouds | Tanks and Temples | RA-MVSNet | https://arxiv.org/abs/2304.13614v2 | Mean F1 (Intermediate) | 65.72 |
Point Clouds | Tanks and Temples | RA-MVSNet | https://arxiv.org/abs/2304.13614v2 | Mean F1 (Advanced) | 39.93 |
Point Clouds | Tanks and Temples | APD-MVS | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Adaptive_Patch_Deformation_for_Textureless-Resilient_Multi-View_Stereo_CVPR_2023_paper.html | Mean F1 (Intermediate) | 63.64 |
Point Clouds | Tanks and Temples | APD-MVS | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Adaptive_Patch_Deformation_for_Textureless-Resilient_Multi-View_Stereo_CVPR_2023_paper.html | Mean F1 (Advanced) | 39.91 |
Point Clouds | Tanks and Temples | GC-MVSNet | https://arxiv.org/abs/2310.19583v3 | Mean F1 (Intermediate) | 62.74 |
Point Clouds | Tanks and Temples | GC-MVSNet | https://arxiv.org/abs/2310.19583v3 | Mean F1 (Advanced) | 38.74 |
Point Clouds | Tanks and Temples | EPP-MVSNet | http://openaccess.thecvf.com//content/ICCV2021/html/Ma_EPP-MVSNet_Epipolar-Assembling_Based_Depth_Prediction_for_Multi-View_Stereo_ICCV_2021_paper.html | Mean F1 (Intermediate) | 61.68 |
Point Clouds | Tanks and Temples | EPP-MVSNet | http://openaccess.thecvf.com//content/ICCV2021/html/Ma_EPP-MVSNet_Epipolar-Assembling_Based_Depth_Prediction_for_Multi-View_Stereo_ICCV_2021_paper.html | Mean F1 (Advanced) | 35.72 |
Point Clouds | Tanks and Temples | ACMM | http://arxiv.org/abs/1904.08103v1 | Mean F1 (Intermediate) | 57.27 |
Point Clouds | Tanks and Temples | ACMM | http://arxiv.org/abs/1904.08103v1 | Mean F1 (Advanced) | 34.02 |
Point Clouds | Tanks and Temples | PatchmatchNet | https://arxiv.org/abs/2012.01411v1 | Mean F1 (Intermediate) | 53.15 |
Point Clouds | Tanks and Temples | PatchmatchNet | https://arxiv.org/abs/2012.01411v1 | Mean F1 (Advanced) | 32.31 |
Point Clouds | Tanks and Temples | IB-MVS | https://arxiv.org/abs/2111.14420v1 | Mean F1 (Intermediate) | 56.02 |
Point Clouds | Tanks and Temples | IB-MVS | https://arxiv.org/abs/2111.14420v1 | Mean F1 (Advanced) | 31.96 |
Point Clouds | Tanks and Temples | Cas-MVSNet | https://arxiv.org/abs/1912.06378v3 | Mean F1 (Intermediate) | 56.84 |
Point Clouds | Tanks and Temples | Cas-MVSNet | https://arxiv.org/abs/1912.06378v3 | Mean F1 (Advanced) | 31.12 |
Point Clouds | Tanks and Temples | COLMAP | http://openaccess.thecvf.com/content_cvpr_2016/html/Schonberger_Structure-From-Motion_Revisited_CVPR_2016_paper.html | Mean F1 (Intermediate) | 42.14 |
Point Clouds | Tanks and Temples | COLMAP | http://openaccess.thecvf.com/content_cvpr_2016/html/Schonberger_Structure-From-Motion_Revisited_CVPR_2016_paper.html | Mean F1 (Advanced) | 27.24 |
Point Clouds | Tanks and Temples | CDS-MVSNet | https://arxiv.org/abs/2112.05999v3 | Mean F1 (Intermediate) | 61.58 |
Point Clouds | Tanks and Temples | AA-RMVSNet | https://arxiv.org/abs/2108.03824v1 | Mean F1 (Intermediate) | 61.51 |
Point Clouds | Tanks and Temples | GBi-Net | https://arxiv.org/abs/2112.02338v1 | Mean F1 (Intermediate) | 61.42 |
Point Clouds | Tanks and Temples | Vis-MVSNet | https://arxiv.org/abs/2008.07928v2 | Mean F1 (Intermediate) | 60.03 |
Point Clouds | Tanks and Temples | UCSNet | https://arxiv.org/abs/1911.12012v2 | Mean F1 (Intermediate) | 54.83 |
Point Clouds | Tanks and Temples | CVP-MVSNet | https://arxiv.org/abs/1912.08329v3 | Mean F1 (Intermediate) | 54.03 |
Point Clouds | Tanks and Temples | MVSNet | http://arxiv.org/abs/1804.02505v2 | Mean F1 (Intermediate) | 43.48 |
Transferability | classification benchmark | ETran | https://arxiv.org/abs/2308.02027v1 | Kendall's Tau | 0.562 |
Transferability | classification benchmark | SFDA | https://arxiv.org/abs/2207.03036v2 | Kendall's Tau | 0.502 |
Transferability | classification benchmark | logme | https://arxiv.org/abs/2102.11005v3 | Kendall's Tau | 0.482 |
Transferability | classification benchmark | LEEP | https://arxiv.org/abs/2002.12462v2 | Kendall's Tau | 0.390 |
Transferability | classification benchmark | PACTran | https://arxiv.org/abs/2203.05126v2 | Kendall's Tau | 0.266 |
Transferability | classification benchmark | NLEEP | https://arxiv.org/abs/2011.11200v4 | Kendall's Tau | 0.228 |
10-shot image generation | Babies | AdAM | https://arxiv.org/abs/2210.16559v3 | FID | 48.83 |
10-shot image generation | Babies | DCL | https://arxiv.org/abs/2205.03805v2 | FID | 56.48 |
10-shot image generation | Babies | CDC | https://arxiv.org/abs/2104.06820v1 | FID | 69.13 |
10-shot image generation | Babies | EWC | https://arxiv.org/abs/2012.02780v1 | FID | 79.93 |
10-shot image generation | Babies | FreezeD | https://arxiv.org/abs/2002.10964v2 | FID | 96.25 |
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