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 ⌀ |
|---|---|---|---|---|---|
Machine Translation > Multimodal Machine Translation | Multi30K | Transformer | https://arxiv.org/abs/1706.03762v7 | BLUE (DE-EN) | 29.0 |
Machine Translation > Multimodal Machine Translation | Multi30K | del | https://arxiv.org/abs/1906.07701v1 | Meteor (EN-FR) | 74.6 |
Machine Translation > Multimodal Machine Translation | Hindi Visual Genome (Test Set) | ViTA | https://arxiv.org/abs/2106.00250v3 | BLEU (EN-HI) | 44.6 |
Machine Translation > Multimodal Machine Translation | Hindi Visual Genome (Challenge Set) | ViTA | https://arxiv.org/abs/2106.00250v3 | BLEU (EN-HI) | 51.6 |
Machine Translation > Multimodal Machine Translation > Multimodal Lexical Translation | MultiSubs English-Spanish | Multimodal BRNN | https://arxiv.org/abs/2103.01910v3 | ALI | 0.81 |
Machine Translation > Multimodal Machine Translation > Multimodal Lexical Translation | MultiSubs English-German | Multimodal BRNN | https://arxiv.org/abs/2103.01910v3 | ALI | 0.94 |
Machine Translation > Multimodal Machine Translation > Multimodal Lexical Translation | MultiSubs English-French | Multimodal BRNN | https://arxiv.org/abs/2103.01910v3 | ALI | 0.81 |
Machine Translation > Multimodal Machine Translation > Multimodal Lexical Translation | MultiSubs English-Portuguese | Multimodal BRNN | https://arxiv.org/abs/2103.01910v3 | ALI | 0.80 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | BERT-fused NMT | https://arxiv.org/abs/2002.06823v1 | BLEU | 38.27 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 37.5 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 36.2 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 33.4 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 32.6 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 29.5 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-French | PBSMT + NMT | http://arxiv.org/abs/1804.07755v2 | BLEU | 27.6 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 39.2 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 34.9 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 33.5 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 33.3 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 28.9 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | PBSMT + NMT | http://arxiv.org/abs/1804.07755v2 | BLEU | 27.7 |
Machine Translation > Unsupervised Machine Translation | WMT2014 French-English | SMT | http://arxiv.org/abs/1809.01272v1 | BLEU | 25.9 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-German | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 22.5 |
Machine Translation > Unsupervised Machine Translation | WMT2014 English-German | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 17.0 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 29.7 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 28.3 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 26.9 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 26.4 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 21.7 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | PBSMT + NMT | http://arxiv.org/abs/1804.07755v2 | BLEU | 20.2 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-German | Synthetic bilingual data init | http://arxiv.org/abs/1810.12703v1 | BLEU | 20.0 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English--Romanian | BERT-fused NMT | https://arxiv.org/abs/2002.06823v1 | BLEU | 36.02 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English--Romanian | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 33.3 |
Machine Translation > Unsupervised Machine Translation | WMT2014 German-English | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 27.0 |
Machine Translation > Unsupervised Machine Translation | WMT2014 German-English | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 20.4 |
Machine Translation > Unsupervised Machine Translation | WMT2016 Romanian-English | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 39.5 |
Machine Translation > Unsupervised Machine Translation | WMT2016 Romanian-English | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 33.1 |
Machine Translation > Unsupervised Machine Translation | WMT2016 Romanian-English | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 31.8 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 40.6 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 35.2 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | SMT + NMT (tuning and joint refinement) | https://arxiv.org/abs/1902.01313v2 | BLEU | 34.4 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 34.3 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | Synthetic bilingual data init | http://arxiv.org/abs/1810.12703v1 | BLEU | 26.7 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | SMT as posterior regularization | http://arxiv.org/abs/1901.04112v1 | BLEU | 26.3 |
Machine Translation > Unsupervised Machine Translation | WMT2016 German-English | PBSMT | http://arxiv.org/abs/1804.07755v2 | BLEU | 25.2 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-Romanian | GPT-3 175B (Few-Shot) | https://arxiv.org/abs/2005.14165v4 | BLEU | 21 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-Romanian | MLM pretraining for encoder and decoder | http://arxiv.org/abs/1901.07291v1 | BLEU | 33.3 |
Machine Translation > Unsupervised Machine Translation | WMT2016 English-Romanian | MASS (6-layer Transformer) | https://arxiv.org/abs/1905.02450v5 | BLEU | 35.2 |
Machine Translation > Low-Resource Neural Machine Translation | Umsuka | https://huggingface.co/MUNasir/umsuka-en-zu | https://arxiv.org/abs/2205.08621v1 | BLEU | 13.73 |
Medical Image Segmentation | Kvasir-SEG | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mean Dice | 0.9502 |
Medical Image Segmentation | Kvasir-SEG | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | mIoU | 0.9051 |
Medical Image Segmentation | Kvasir-SEG | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | Precision | 0.9628 |
Medical Image Segmentation | Kvasir-SEG | DUCK-Net | https://arxiv.org/abs/2311.02239v1 | Recall | 0.9379 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B5 | https://arxiv.org/abs/2407.16298v1 | mean Dice | 0.9488 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B5 | https://arxiv.org/abs/2407.16298v1 | mIoU | 0.9065 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B5 | https://arxiv.org/abs/2407.16298v1 | F-measure | 0.9513 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B5 | https://arxiv.org/abs/2407.16298v1 | Precision | 0.9713 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B5 | https://arxiv.org/abs/2407.16298v1 | Recall | 0.9321 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B4 | https://arxiv.org/abs/2407.16298v1 | mean Dice | 0.9483 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B4 | https://arxiv.org/abs/2407.16298v1 | mIoU | 0.9056 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B4 | https://arxiv.org/abs/2407.16298v1 | F-measure | 0.9552 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B4 | https://arxiv.org/abs/2407.16298v1 | Precision | 0.9679 |
Medical Image Segmentation | Kvasir-SEG | EffiSegNet-B4 | https://arxiv.org/abs/2407.16298v1 | Recall | 0.9429 |
Medical Image Segmentation | Kvasir-SEG | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mean Dice | 0.947 |
Medical Image Segmentation | Kvasir-SEG | SegMed | https://link.springer.com/chapter/10.1007/978-3-031-80507-3_12 | mIoU | 0.899 |
Medical Image Segmentation | Kvasir-SEG | FCB Former | https://arxiv.org/abs/2207.07842v1 | mean Dice | 0.9445 |
Medical Image Segmentation | Kvasir-SEG | FCB Former | https://arxiv.org/abs/2207.07842v1 | mIoU | 0.8974 |
Medical Image Segmentation | Kvasir-SEG | FCB-SwinV2 Transformer | https://arxiv.org/abs/2302.01027v1 | mean Dice | 0.9420 |
Medical Image Segmentation | Kvasir-SEG | FCB-SwinV2 Transformer | https://arxiv.org/abs/2302.01027v1 | mIoU | 0.8973 |
Medical Image Segmentation | Kvasir-SEG | SEP | https://arxiv.org/abs/2211.08284v3 | mean Dice | 0.9411 |
Medical Image Segmentation | Kvasir-SEG | SEP | https://arxiv.org/abs/2211.08284v3 | mIoU | 0.9002 |
Medical Image Segmentation | Kvasir-SEG | LM-Net | https://arxiv.org/abs/2501.03838v1 | mean Dice | 0.9409 |
Medical Image Segmentation | Kvasir-SEG | LM-Net | https://arxiv.org/abs/2501.03838v1 | mIoU | 0.8912 |
Medical Image Segmentation | Kvasir-SEG | LM-Net | https://arxiv.org/abs/2501.03838v1 | Precision | 0.8964 |
Medical Image Segmentation | Kvasir-SEG | LM-Net | https://arxiv.org/abs/2501.03838v1 | Recall | 0.9038 |
Medical Image Segmentation | Kvasir-SEG | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mean Dice | 0.939 |
Medical Image Segmentation | Kvasir-SEG | RAPUNet | https://ieeexplore.ieee.org/document/10681057 | mIoU | 0.885 |
Medical Image Segmentation | Kvasir-SEG | FCBFormer | https://arxiv.org/abs/2208.08352v1 | mean Dice | 0.9385 |
Medical Image Segmentation | Kvasir-SEG | FCBFormer | https://arxiv.org/abs/2208.08352v1 | mIoU | 0.8903 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-DFUS | https://arxiv.org/abs/2209.07313v1 | mean Dice | 0.9363 |
Medical Image Segmentation | Kvasir-SEG | HarDNet-DFUS | https://arxiv.org/abs/2209.07313v1 | mIoU | 0.8894 |
Medical Image Segmentation | Kvasir-SEG | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mean Dice | 0.9357 |
Medical Image Segmentation | Kvasir-SEG | SSFormer-L | https://arxiv.org/abs/2203.03635v3 | mIoU | 0.8905 |
Medical Image Segmentation | Kvasir-SEG | FCBFormer | https://arxiv.org/abs/2412.13156v1 | mean Dice | 0.932 |
Medical Image Segmentation | Kvasir-SEG | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mean Dice | 0.931 |
Medical Image Segmentation | Kvasir-SEG | ESFPNet-L | https://arxiv.org/abs/2207.07759v3 | mIoU | 0.887 |
Medical Image Segmentation | Kvasir-SEG | UGCANet | https://arxiv.org/abs/2307.06260v1 | mean Dice | 0.928 |
Medical Image Segmentation | Kvasir-SEG | UGCANet | https://arxiv.org/abs/2307.06260v1 | mIoU | 0.881 |
Medical Image Segmentation | Kvasir-SEG | EMCAD | https://arxiv.org/abs/2405.06880v1 | mean Dice | 0.928 |
Medical Image Segmentation | Kvasir-SEG | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mean Dice | 0.9274 |
Medical Image Segmentation | Kvasir-SEG | PVT-GCASCADE | https://arxiv.org/abs/2310.16175v1 | mIoU | 0.8790 |
Medical Image Segmentation | Kvasir-SEG | ColonFormer | https://arxiv.org/abs/2205.08473v3 | mean Dice | 0.927 |
Medical Image Segmentation | Kvasir-SEG | ColonFormer | https://arxiv.org/abs/2205.08473v3 | mIoU | 0.877 |
Medical Image Segmentation | Kvasir-SEG | RaBiT | https://arxiv.org/abs/2307.06420v1 | mean Dice | 0.927 |
Medical Image Segmentation | Kvasir-SEG | RaBiT | https://arxiv.org/abs/2307.06420v1 | mIoU | 0.873 |
Medical Image Segmentation | Kvasir-SEG | GMSRF-Net | https://arxiv.org/abs/2111.10614v1 | mean Dice | 0.9263 |
Medical Image Segmentation | Kvasir-SEG | GMSRF-Net | https://arxiv.org/abs/2111.10614v1 | mIoU | 0.8843 |
Medical Image Segmentation | Kvasir-SEG | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mean Dice | 0.9258 |
Medical Image Segmentation | Kvasir-SEG | PVT-CASCADE | https://openaccess.thecvf.com/content/WACV2023/html/Rahman_Medical_Image_Segmentation_via_Cascaded_Attention_Decoding_WACV_2023_paper.html | mIoU | 0.8776 |
Medical Image Segmentation | Kvasir-SEG | DuAT | https://arxiv.org/abs/2212.11677v1 | Average MAE | 0.023 |
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