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 ⌀ |
|---|---|---|---|---|---|
answerability prediction | PeerQA | Command-R-v01-34B-128k | null | Macro F1 | 0.4197 |
answerability prediction | PeerQA | GPT-3.5-Turbo-0613-16k | https://arxiv.org/abs/2005.14165v4 | Macro F1 | 0.3304 |
answerability prediction | PeerQA | Llama-3-IT-8B-8k | https://arxiv.org/abs/2407.21783v3 | Macro F1 | 0.3112 |
answerability prediction | PeerQA | GPT-4o-2024-08-06 | https://arxiv.org/abs/2303.08774v5 | Macro F1 | 0.3087 |
answerability prediction | PeerQA | Llama-3-IT-8B-32k | https://arxiv.org/abs/2407.21783v3 | Macro F1 | 0.2881 |
Image Colorization | CelebA | DDRM | https://arxiv.org/abs/2212.00490v2 | Consistency | 455.9 |
Image Colorization | CelebA | DDRM | https://arxiv.org/abs/2212.00490v2 | FID | 31.26 |
Image Colorization | CelebA | DDNM | https://arxiv.org/abs/2212.00490v2 | Consistency | 26.25 |
Image Colorization | CelebA | DDNM | https://arxiv.org/abs/2212.00490v2 | FID | 26.44 |
Image Colorization | CelebA | A+y | https://arxiv.org/abs/2212.00490v2 | Consistency | 0 |
Image Colorization | CelebA | A+y | https://arxiv.org/abs/2212.00490v2 | FID | 68.81 |
Image Colorization | ImageNet | DDRM | https://arxiv.org/abs/2212.00490v2 | Consistency | 260.4 |
Image Colorization | ImageNet | DDRM | https://arxiv.org/abs/2212.00490v2 | FID | 36.56 |
Image Colorization | ImageNet | DDNM | https://arxiv.org/abs/2212.00490v2 | Consistency | 42.32 |
Image Colorization | ImageNet | DDNM | https://arxiv.org/abs/2212.00490v2 | FID | 36.32 |
Image Colorization | ImageNet | A+y | https://arxiv.org/abs/2212.00490v2 | Consistency | 0 |
Image Colorization | ImageNet | A+y | https://arxiv.org/abs/2212.00490v2 | FID | 43.37 |
Image Colorization | ImageNet | DGP | https://arxiv.org/abs/2212.00490v2 | FID | 69.54 |
Image Colorization | NIR2RGB VCIP Challange Dataset | ColorMamba | https://arxiv.org/abs/2408.08087v1 | PSNR | 24.56 |
Image Colorization | NIR2RGB VCIP Challange Dataset | CoColor | https://arxiv.org/abs/2308.03348v1 | PSNR | 23.54 |
Image Colorization | NIR2RGB VCIP Challange Dataset | CycleGAN | https://arxiv.org/abs/1703.10593v7 | PSNR | 19.59 |
Attribute Mining | OA-Mine - annotations | T5 Large - End2End | https://arxiv.org/abs/2407.01137v1 | F1-score | 86.28 |
Attribute Mining | AE-110k | T5 Large - End2End | https://arxiv.org/abs/2407.01137v1 | F1-score | 84.29 |
Attribute Mining | MAVE | T5 Large - End2End | https://arxiv.org/abs/2407.01137v1 | F1-score | 95.19 |
Disjoint 19-1 | PASCAL VOC 2012 | MBS | https://arxiv.org/abs/2407.11859v1 | mIoU | 82.8 |
Automatic Speech Recognition (ASR) | LRS2 | Whisper | https://arxiv.org/abs/2406.10082v3 | Test WER | 1.3 |
Automatic Speech Recognition (ASR) | LRS2 | CTC/Attention | https://arxiv.org/abs/2303.14307v3 | Test WER | 1.5 |
Automatic Speech Recognition (ASR) | LRS2 | MoCo + wav2vec (w/o extLM) | https://arxiv.org/abs/2203.07996v2 | Test WER | 2.7 |
Automatic Speech Recognition (ASR) | LRS2 | End2end Conformer | https://arxiv.org/abs/2102.06657v1 | Test WER | 3.9 |
Automatic Speech Recognition (ASR) | LRS2 | Whisper-LLaMA | https://arxiv.org/abs/2310.06434v2 | Test WER | 6.6 |
Automatic Speech Recognition (ASR) | LRS2 | LF-MMI TDNN | https://arxiv.org/abs/2001.01656v1 | Test WER | 6.7 |
Automatic Speech Recognition (ASR) | LRS2 | CTC/attention | http://arxiv.org/abs/1810.00108v1 | Test WER | 8.2 |
Automatic Speech Recognition (ASR) | LRS2 | TM-seq2seq | http://arxiv.org/abs/1809.02108v2 | Test WER | 9.7 |
Automatic Speech Recognition (ASR) | LRS2 | TM-CTC | http://arxiv.org/abs/1809.02108v2 | Test WER | 10.1 |
Automatic Speech Recognition (ASR) | LRS3-TED | DistillAV | https://arxiv.org/abs/2502.05766v1 | WER | 1.4 |
Automatic Speech Recognition (ASR) | LRS3-TED | CTC/Attention | https://arxiv.org/abs/2303.14307v3 | Word Error Rate (WER) | 1 |
Automatic Speech Recognition (ASR) | The Spoken Wikipedia Corpora | Conformer Transducer | https://ieeexplore.ieee.org/abstract/document/9854978/ | WER (%) | 8.04% |
Automatic Speech Recognition (ASR) | Sagalee | Whisper-largev3-finetuned | https://arxiv.org/abs/2502.00421v1 | Test WER | 10.82 |
Automatic Speech Recognition (ASR) | Sagalee | Conformer | https://arxiv.org/abs/2502.00421v1 | Test WER | 15.32 |
Automatic Speech Recognition (ASR) | RealMAN | CleanMel-L-mask | https://arxiv.org/abs/2502.20040v1 | CER | 14.4 |
Automatic Speech Recognition (ASR) | RealMAN | SpatialNet | null | CER | 14.5 |
Automatic Speech Recognition (ASR) | HUI speech corpus | Conformer Transducer | https://ieeexplore.ieee.org/abstract/document/9854978/ | WER (%) | 1.89% |
Automatic Speech Recognition (ASR) | Voxforge German | Conformer Transducer | https://ieeexplore.ieee.org/abstract/document/9854978/ | WER (%) | 3.36% |
Automatic Speech Recognition (ASR) | M-AILabs speech dataset | Conformer Transducer | https://ieeexplore.ieee.org/abstract/document/9854978/ | WER (%) | 4.28% |
Automatic Speech Recognition (ASR) | VoxPopuli | Conformer Transducer (German) | https://ieeexplore.ieee.org/abstract/document/9854978/ | WER (%) | 8.98% |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (throat microphone) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.073 |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (headset microphone) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.028 |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (forehead accelerometer) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.046 |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (soft in-ear microphone) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.041 |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (rigid in-ear microphone) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.045 |
Automatic Speech Recognition (ASR) > Automatic Phoneme Recognition | VibraVox (temple vibration pickup) | medium wav2vec2.0 | https://arxiv.org/abs/2407.11828v4 | Test PER | 0.142 |
Stereo Disparity Estimation | Scene Flow | AANet | https://arxiv.org/abs/2004.09548v1 | EPE | 0.87 |
Stereo Disparity Estimation | Scene Flow | AANet | https://arxiv.org/abs/2004.09548v1 | one pixel error | 9.3 |
Stereo Disparity Estimation | Scene Flow | LEAStereo | https://arxiv.org/abs/2010.13501v1 | EPE | 0.78 |
Stereo Disparity Estimation | Scene Flow | LEAStereo | https://arxiv.org/abs/2010.13501v1 | one pixel error | 7.82 |
Stereo Disparity Estimation | Scene Flow | AANet+ | https://arxiv.org/abs/2004.09548v1 | EPE | 0.72 |
Stereo Disparity Estimation | Scene Flow | AANet+ | https://arxiv.org/abs/2004.09548v1 | one pixel error | 7.4 |
Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | https://arxiv.org/abs/2007.03085v2 | EPE | 0.7 |
Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | https://arxiv.org/abs/2007.03085v2 | one pixel error | 7.7 |
Stereo Disparity Estimation | Scene Flow | CDN-GANet Deep | https://arxiv.org/abs/2007.03085v2 | three pixel error | 2.98 |
Stereo Disparity Estimation | Scene Flow | HITNet | https://arxiv.org/abs/2007.12140v5 | EPE | 0.529 |
Stereo Disparity Estimation | Scene Flow | HITNet | https://arxiv.org/abs/2007.12140v5 | one pixel error | 5.52 |
Stereo Disparity Estimation | Scene Flow | HITNet | https://arxiv.org/abs/2007.12140v5 | three pixel error | 3.00 |
Stereo Disparity Estimation | Scene Flow | HITNet L | https://arxiv.org/abs/2007.12140v5 | EPE | 0.43 |
Stereo Disparity Estimation | Scene Flow | HITNet L | https://arxiv.org/abs/2007.12140v5 | one pixel error | 4.70 |
Stereo Disparity Estimation | Scene Flow | HITNet L | https://arxiv.org/abs/2007.12140v5 | three pixel error | 2.57 |
Stereo Disparity Estimation | Scene Flow | HITNet XL | https://arxiv.org/abs/2007.12140v5 | EPE | 0.36 |
Stereo Disparity Estimation | Scene Flow | HITNet XL | https://arxiv.org/abs/2007.12140v5 | one pixel error | 4.09 |
Stereo Disparity Estimation | Scene Flow | HITNet XL | https://arxiv.org/abs/2007.12140v5 | three pixel error | 2.21 |
Stereo Disparity Estimation | KITTI 2015 | MoCha-Stereo | https://arxiv.org/abs/2404.06842v4 | D1-all | 1.53 |
Stereo Disparity Estimation | KITTI 2015 | DSM | https://arxiv.org/abs/2008.04800v1 | D1-all | 2.28 |
Stereo Disparity Estimation | Middlebury 2014 | MoCha-V2 | https://arxiv.org/abs/2404.06842v4 | D1 Error (2px) | 3.51 |
Stereo Disparity Estimation | Middlebury 2014 | RAFT-Stereo | https://arxiv.org/abs/2109.07547v1 | D1 Error (2px) | 4.74 |
Image Segmentation | MSD Heart | OneNete,4 | https://arxiv.org/abs/2411.09838v1 | mIoU | 6.6 |
Image Segmentation | MAS3K | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | S-measure | 0.903 |
Image Segmentation | MAS3K | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | mIoU | 0.799 |
Image Segmentation | MAS3K | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | E-measure | 0.943 |
Image Segmentation | MAS3K | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | MAE | 0.021 |
Image Segmentation | MAS3K | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | S-measure | 0.887 |
Image Segmentation | MAS3K | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | mIoU | 0.788 |
Image Segmentation | MAS3K | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | E-measure | 0.938 |
Image Segmentation | MAS3K | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | MAE | 0.025 |
Image Segmentation | MAS3K | MASNet | https://ieeexplore.ieee.org/document/10113781 | S-measure | 0.864 |
Image Segmentation | MAS3K | MASNet | https://ieeexplore.ieee.org/document/10113781 | mIoU | 0.742 |
Image Segmentation | MAS3K | MASNet | https://ieeexplore.ieee.org/document/10113781 | E-measure | 0.906 |
Image Segmentation | MAS3K | MASNet | https://ieeexplore.ieee.org/document/10113781 | MAE | 0.032 |
Image Segmentation | MAS3K | ZoomNet | https://arxiv.org/abs/2203.02688v1 | S-measure | 0.862 |
Image Segmentation | MAS3K | ZoomNet | https://arxiv.org/abs/2203.02688v1 | mIoU | 0.736 |
Image Segmentation | MAS3K | ZoomNet | https://arxiv.org/abs/2203.02688v1 | E-measure | 0.898 |
Image Segmentation | MAS3K | ZoomNet | https://arxiv.org/abs/2203.02688v1 | MAE | 0.032 |
Image Segmentation | OxfordPets | OneNete,4-C | https://arxiv.org/abs/2411.09838v1 | Dice Score | 0.967 |
Image Segmentation | RMAS | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | S-measure | 0.865 |
Image Segmentation | RMAS | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | mIoU | 0.742 |
Image Segmentation | RMAS | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | E-measure | 0.948 |
Image Segmentation | RMAS | MAS-SAM | https://arxiv.org/abs/2404.15700v2 | MAE | 0.021 |
Image Segmentation | RMAS | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | S-measure | 0.874 |
Image Segmentation | RMAS | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | mIoU | 0.738 |
Image Segmentation | RMAS | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | E-measure | 0.944 |
Image Segmentation | RMAS | SAM2-UNet | https://arxiv.org/abs/2408.08870v1 | MAE | 0.022 |
Image Segmentation | RMAS | MASNet | https://ieeexplore.ieee.org/document/10113781 | S-measure | 0.862 |
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