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 ⌀ |
|---|---|---|---|---|---|
Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | sensitivity | 0.7119 |
Retinal Vessel Segmentation | DRIVE | DR_2021 | https://arxiv.org/abs/2207.04345v1 | Specificity | 0.9832 |
Retinal Vessel Segmentation | DRIVE | ET-Net | https://arxiv.org/abs/1907.10936v1 | Accuracy | 0.956 |
Retinal Vessel Segmentation | DRIVE | ET-Net | https://arxiv.org/abs/1907.10936v1 | mIoU | 0.7744 |
Retinal Vessel Segmentation | INSPIRE-AVR (LUNet subset) | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice | 75.6 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 83.2 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Junior Ophtalmologist | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 82.6 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | VascX | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 80.6 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Automorph | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 74.0 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | UZLF | Little W-Net | https://arxiv.org/abs/2409.16016v2 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 60.9 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | HRF | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9783 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | RITE/DRIVE | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9666 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | LES-AV | RRWNet | https://arxiv.org/abs/2402.03166v5 | Accuracy | 0.9481 |
Retinal Vessel Segmentation > Artery/Veins Retinal Vessel Segmentation | INSPIRE-AVR (LUNet subset) | LUNet | https://arxiv.org/abs/2309.05780v1 | Average Dice (0.5*Dice_a + 0.5*Dice_v) | 75.6 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | GPT-4 | 57.06 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Sentence-BERT | 49.54 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | SimCSE | 51.39 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Correctness | 3.50 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Hallucination | 0.71 |
3D Object Captioning | Objaverse | MiniGPT-3D | https://arxiv.org/abs/2405.01413v1 | Precision | 83.14 |
3D Object Captioning | Objaverse | ShapeLLM-13B | https://arxiv.org/abs/2402.17766v3 | GPT-4 | 48.94 |
3D Object Captioning | Objaverse | ShapeLLM-13B | https://arxiv.org/abs/2402.17766v3 | Sentence-BERT | 48.52 |
3D Object Captioning | Objaverse | ShapeLLM-13B | https://arxiv.org/abs/2402.17766v3 | SimCSE | 49.98 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | GPT-4 | 48.15 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | Sentence-BERT | 47.91 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | SimCSE | 49.12 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | Correctness | 3.10 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | Hallucination | 0.84 |
3D Object Captioning | Objaverse | PointLLM-13B V1.2 | https://arxiv.org/abs/2308.16911v3 | Precision | 78.75 |
3D Object Captioning | Objaverse | ShapeLLM-7B | https://arxiv.org/abs/2402.17766v3 | GPT-4 | 46.92 |
3D Object Captioning | Objaverse | ShapeLLM-7B | https://arxiv.org/abs/2402.17766v3 | Sentence-BERT | 48.20 |
3D Object Captioning | Objaverse | ShapeLLM-7B | https://arxiv.org/abs/2402.17766v3 | SimCSE | 49.23 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | GPT-4 | 44.85 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | Sentence-BERT | 47.47 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | SimCSE | 48.55 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | Correctness | 3.04 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | Hallucination | 0.66 |
3D Object Captioning | Objaverse | PointLLM-7B V1.2 | https://arxiv.org/abs/2308.16911v3 | Precision | 82.14 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | GPT-4 | 33.42 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Sentence-BERT | 44.48 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | SimCSE | 43.68 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Correctness | 1.77 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Hallucination | 1.16 |
3D Object Captioning | Objaverse | 3D-LLM | https://arxiv.org/abs/2307.12981v1 | Precision | 60.39 |
NLP based Person Retrival > Decoder | ^(#$!@#$)(()))****** | peacock return policy | https://arxiv.org/abs/2010.10348v2 | 0-shot MRR | 13 |
NLP based Person Retrival > Decoder | ^(#$!@#$)(()))****** | e | https://arxiv.org/abs/2310.02992v3 | 0..5sec | w |
Grounded Multimodal Named Entity Recognition | Twitter-GMNER | RiVEG | https://arxiv.org/abs/2402.09989v4 | F1 | 67.06 |
Runtime ranking | TpuGraphs Layout mean | TGraph | https://arxiv.org/abs/2405.16623v2 | Kendall's Tau | 0.674 |
Runtime ranking | TpuGraphs Layout mean | TpuGraphs | https://arxiv.org/abs/2308.13490v3 | Kendall's Tau | 0.298 |
Segmentation | MMFlood | ResNet50 + DeepLabV3+ | https://ieeexplore.ieee.org/document/9882096 | F1 score | 0.7714 |
Segmentation | SimGas | LangGas | https://arxiv.org/abs/2503.02910v1 | IoU | 0.69 |
Segmentation | SimGas | LangGas | https://arxiv.org/abs/2503.02910v1 | Precision | 0.82 |
Segmentation | SimGas | LangGas | https://arxiv.org/abs/2503.02910v1 | Recall | 0.82 |
Segmentation | SA-1B | unSAM+ (Semi-supervised) | https://arxiv.org/abs/2406.20081v1 | Average Precision | 42.8 |
Segmentation | SA-1B | unSAM+ (Semi-supervised) | https://arxiv.org/abs/2406.20081v1 | AR-small | 36.2 |
Segmentation | SA-1B | unSAM+ (Semi-supervised) | https://arxiv.org/abs/2406.20081v1 | AR-medium | 65.9 |
Segmentation | SA-1B | unSAM+ (Semi-supervised) | https://arxiv.org/abs/2406.20081v1 | AR-large | 76.5 |
Segmentation | SA-1B | SAM | https://arxiv.org/abs/2406.20081v1 | Average Precision | 38.9 |
Segmentation | SA-1B | SAM | https://arxiv.org/abs/2406.20081v1 | AR-small | 20.0 |
Segmentation | SA-1B | SAM | https://arxiv.org/abs/2406.20081v1 | AR-medium | 59.9 |
Segmentation | SA-1B | SAM | https://arxiv.org/abs/2406.20081v1 | AR-large | 82.8 |
Segmentation | !(()&&!|*|*| | HNN | http://arxiv.org/abs/1910.10504v1 | 10% | 20 |
Segmentation | MFSD | ABANet | https://onlinelibrary.wiley.com/doi/10.1111/exsy.13625 | F1 Score | 96.817% |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned with document splitting | https://arxiv.org/abs/2405.15729v2 | Correctness, max., % | 42 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned with document splitting | https://arxiv.org/abs/2405.15729v2 | Correctness, avg., % | 34 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned with document splitting | https://arxiv.org/abs/2405.15729v2 | Validness, avg., % | 69.1 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned with document splitting | https://arxiv.org/abs/2405.15729v2 | Validness, max., % | 76 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned at 4096 tokens | https://arxiv.org/abs/2405.15729v2 | Correctness, max., % | 45 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned at 4096 tokens | https://arxiv.org/abs/2405.15729v2 | Correctness, avg., % | 32 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned at 4096 tokens | https://arxiv.org/abs/2405.15729v2 | Validness, avg., % | 63.1 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B, fine-tuned at 4096 tokens | https://arxiv.org/abs/2405.15729v2 | Validness, max., % | 84 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B | https://arxiv.org/abs/2405.15729v2 | Correctness, max., % | 36 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B | https://arxiv.org/abs/2405.15729v2 | Correctness, avg., % | 31.1 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B | https://arxiv.org/abs/2405.15729v2 | Validness, avg., % | 60.7 |
OpenAPI code completion | OpenAPI completion refined | Code Llama 7B | https://arxiv.org/abs/2405.15729v2 | Validness, max., % | 64 |
OpenAPI code completion | OpenAPI completion refined | GitHub Copilot | https://arxiv.org/abs/2405.15729v2 | Correctness, max., % | 29 |
OpenAPI code completion | OpenAPI completion refined | GitHub Copilot | https://arxiv.org/abs/2405.15729v2 | Correctness, avg., % | 29 |
OpenAPI code completion | OpenAPI completion refined | GitHub Copilot | https://arxiv.org/abs/2405.15729v2 | Validness, avg., % | 68 |
OpenAPI code completion | OpenAPI completion refined | GitHub Copilot | https://arxiv.org/abs/2405.15729v2 | Validness, max., % | 68 |
Segmented Multimodal Named Entity Recognition | Twitter-SMNER | RiVEG | https://arxiv.org/abs/2406.07268v1 | F1 | 63.92 |
Vietnamese Natural Language Inference | ViNLI | XLM-R-large | https://aclanthology.org/2022.coling-1.339 | 3-class test accuracy | 81.36 |
Vietnamese Natural Language Inference | ViNLI | XLM-R-large | https://aclanthology.org/2022.coling-1.339 | 4-class test accuracy | 85.99 |
Vietnamese Natural Language Inference | ViNLI | CafeBERT | https://arxiv.org/abs/2403.15882v1 | 4-class test accuracy | 86.11 |
Binary text classification | TURINGBENCH (Turing Test, FAIR_wmt20) | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | F1 score | 0.9966 |
Binary text classification | TURINGBENCH (Turing Test, FAIR_wmt20) | RoBERTa | https://arxiv.org/abs/2109.13296v1 | F1 score | 0.4531 |
Binary text classification | TURINGBENCH (Turing Test, GPT-3) | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | F1 score | 0.9709 |
Binary text classification | TURINGBENCH (Turing Test, GPT-3) | RoBERTa | https://arxiv.org/abs/2109.13296v1 | F1 score | 0.5209 |
Binary text classification | ECHR Non-Anonymized | HIER-BERT | https://arxiv.org/abs/1906.02059v1 | Macro F1 | 82.0 |
Binary text classification | MAGE (Arbitrary-domains & Arbitrary-models) | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | Average Recall | 0.9611 |
Binary text classification | MAGE (Arbitrary-domains & Arbitrary-models) | Longformer | https://arxiv.org/abs/2305.13242v3 | Average Recall | 0.9053 |
Binary text classification | TweepFake | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | F1 score | 0.942 |
Binary text classification | TweepFake | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | Accuracy (%) | 94.3 |
Binary text classification | TweepFake | XLNet | https://arxiv.org/abs/2008.00036v2 | F1 score | 0.882 |
Binary text classification | TweepFake | XLNet | https://arxiv.org/abs/2008.00036v2 | Accuracy (%) | 87.7 |
Binary text classification | Ghostbuster (All Domains) | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | F1 score | 1.0 |
Binary text classification | Ghostbuster (All Domains) | Ghostbuster | https://arxiv.org/abs/2305.15047v3 | F1 score | 0.99 |
Binary text classification | MixSet (Binary) | GigaCheck (Mistral-7B) | https://arxiv.org/abs/2410.23728v2 | F1 score | 0.99 |
Binary text classification | MixSet (Binary) | Radar | https://arxiv.org/abs/2401.05952v2 | F1 score | 0.876 |
Binary text classification > Detection of potentially void clauses | AGB-DE | AGBert | https://arxiv.org/abs/2406.06809v1 | F1 | 0.54 |
answerability prediction | PeerQA | Mistral-IT-v02-7B-32k | https://arxiv.org/abs/2310.06825v1 | Macro F1 | 0.4703 |
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