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 ⌀ |
|---|---|---|---|---|---|
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 3p | 0.221 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 2i | 0.352 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 3i | 0.457 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 ip | 0.129 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 pi | 0.249 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 2u | 0.284 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | FB15k-237 | CQD-Beam | https://arxiv.org/abs/2011.03459v4 | Hits@3 up | 0.121 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | WD50K-NFOL | NQE | https://arxiv.org/abs/2211.13469v3 | AVGp-MRR | 0.3687 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Complex Query Answering | WD50K-NFOL | NQE | https://arxiv.org/abs/2211.13469v3 | AVGn-MRR | 0.1406 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Open Knowledge Graph Canonicalization | Noun Phrase Canonicalization | Galárraga et al., 2014 | null | Base Dataset | 94.8 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Open Knowledge Graph Canonicalization | Noun Phrase Canonicalization | Galárraga et al., 2014 | null | Ambiguous dataset | 97.9 |
Inductive knowledge graph completion > Large Language Model > Knowledge Graphs > Open Knowledge Graph Canonicalization | Noun Phrase Canonicalization | Galárraga et al., 2014 | null | ReVerb45k | 98.3 |
Inductive knowledge graph completion > Large Language Model > RAG | PubMedQA corpus with metadata | MetaGen Blended RAG | https://arxiv.org/abs/2505.18247v1 | ANS-EM | 77.90 |
Inductive knowledge graph completion > Large Language Model > AI Agent > N-Queens Problem - All Possible Solutions | . | Non-Recursive Blind Approach by Ghosh et al | https://doi.org/10.1007/978-981-99-0550-8_27 | Delay (seconds) | 41 for N=14 Queens |
Scene Text Recognition | SVT | CLIP4STR-H (DFN-5B) | https://arxiv.org/abs/2305.14014v4 | Accuracy | 99.1 |
Scene Text Recognition | SVT | DTrOCR 105M | https://arxiv.org/abs/2308.15996v1 | Accuracy | 98.9 |
Scene Text Recognition | SVT | CLIP4STR-B* | https://arxiv.org/abs/2401.00028v3 | Accuracy | 98.76 |
Scene Text Recognition | SVT | MGP-STR | https://arxiv.org/abs/2209.03592v2 | Accuracy | 98.6 |
Scene Text Recognition | SVT | CLIP4STR-L (DataComp-1B) | https://arxiv.org/abs/2305.14014v4 | Accuracy | 98.6 |
Scene Text Recognition | SVT | CPPD | https://arxiv.org/abs/2307.12270v2 | Accuracy | 98.5 |
Scene Text Recognition | SVT | CLIP4STR-L | https://arxiv.org/abs/2305.14014v4 | Accuracy | 98.5 |
Scene Text Recognition | SVT | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | Accuracy | 98.3 |
Scene Text Recognition | SVT | PARSeq | https://arxiv.org/abs/2207.06966v1 | Accuracy | 97.9±0.2 |
Scene Text Recognition | SVT | CCD-ViT-Base(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 97.8 |
Scene Text Recognition | SVT | CCD-ViT-Small(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 96.4 |
Scene Text Recognition | SVT | CCD-ViT-Tiny(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 96.0 |
Scene Text Recognition | SVT | S-GTR | https://arxiv.org/abs/2112.12916v1 | Accuracy | 95.8 |
Scene Text Recognition | SVT | SIGA_T | https://arxiv.org/abs/2203.03382v4 | Accuracy | 95.1 |
Scene Text Recognition | SVT | MATRN | https://arxiv.org/abs/2111.15263v3 | Accuracy | 95 |
Scene Text Recognition | SVT | Yet Another Text Recognizer | https://arxiv.org/abs/2107.13938v1 | Accuracy | 94.7 |
Scene Text Recognition | SVT | NRTR+TPS++ | https://arxiv.org/abs/2305.05322v1 | Accuracy | 94.6 |
Scene Text Recognition | SVT | DPAN | https://dl.acm.org/doi/10.1145/3460426.3463674 | Accuracy | 93.9 |
Scene Text Recognition | SVT | CDistNet (Ours) | https://arxiv.org/abs/2111.11011v5 | Accuracy | 93.82 |
Scene Text Recognition | SVT | DiffusionSTR | https://arxiv.org/abs/2306.16707v1 | Accuracy | 93.6 |
Scene Text Recognition | SVT | RCEED | https://arxiv.org/abs/2106.06960v2 | Accuracy | 91.8 |
Scene Text Recognition | SVT | SRN | https://arxiv.org/abs/2003.12294v1 | Accuracy | 91.5 |
Scene Text Recognition | SVT | SATRN | https://arxiv.org/abs/1910.04396v1 | Accuracy | 91.3 |
Scene Text Recognition | SVT | CSTR | https://arxiv.org/abs/2102.10884v3 | Accuracy | 90.6 |
Scene Text Recognition | SVT | TextScanner | https://arxiv.org/abs/1912.12422v2 | Accuracy | 90.1 |
Scene Text Recognition | SVT | SEED | https://arxiv.org/abs/2005.10977v1 | Accuracy | 89.6 |
Scene Text Recognition | SVT | ASTER | http://122.205.5.5:8071/UpLoadFiles/Papers/ASTER_PAMI18.pdf | Accuracy | 89.5 |
Scene Text Recognition | SVT | DAN | https://arxiv.org/abs/1912.10205v1 | Accuracy | 89.2 |
Scene Text Recognition | SVT | SAFL | https://arxiv.org/abs/2201.00132v1 | Accuracy | 88.6 |
Scene Text Recognition | SVT | ViTSTR | https://arxiv.org/abs/2105.08582v1 | Accuracy | 87.7 |
Scene Text Recognition | SVT | Baek et al. | https://arxiv.org/abs/1904.01906v4 | Accuracy | 87.5 |
Scene Text Recognition | SVT | CA-FCN | http://arxiv.org/abs/1809.06508v2 | Accuracy | 86.4 |
Scene Text Recognition | SVT | SAR | http://arxiv.org/abs/1811.00751v2 | Accuracy | 84.5 |
Scene Text Recognition | SVT | STAR-Net | http://www.bmva.org/bmvc/2016/papers/paper043/paper043.pdf | Accuracy | 83.6 |
Scene Text Recognition | SVT | RARE | http://arxiv.org/abs/1603.03915v2 | Accuracy | 81.9 |
Scene Text Recognition | SVT | CRNN | http://arxiv.org/abs/1507.05717v1 | Accuracy | 80.8 |
Scene Text Recognition | SVT | CHAR | http://arxiv.org/abs/1406.2227v4 | Accuracy | 68.0 |
Scene Text Recognition | IC13 | ABINet-LV+TPS++ | https://arxiv.org/abs/2305.05322v1 | Accuracy | 97.8 |
Scene Text Recognition | SVTP | DTrOCR 105M | https://arxiv.org/abs/2308.15996v1 | Accuracy | 98.6 |
Scene Text Recognition | SVTP | MGP-STR | https://arxiv.org/abs/2209.03592v2 | Accuracy | 98.3 |
Scene Text Recognition | SVTP | CLIP4STR-L* | https://arxiv.org/abs/2401.00028v3 | Accuracy | 98.13 |
Scene Text Recognition | SVTP | CLIP4STR-L (DataComp-1B) | https://arxiv.org/abs/2305.14014v4 | Accuracy | 98.1 |
Scene Text Recognition | SVTP | CLIP4STR-L | https://arxiv.org/abs/2305.14014v4 | Accuracy | 97.4 |
Scene Text Recognition | SVTP | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | Accuracy | 97.2 |
Scene Text Recognition | SVTP | CPPD | https://arxiv.org/abs/2307.12270v2 | Accuracy | 96.7 |
Scene Text Recognition | SVTP | CCD-ViT-Base | https://arxiv.org/abs/2211.00288v4 | Accuracy | 96.1 |
Scene Text Recognition | SVTP | PARSeq | https://arxiv.org/abs/2207.06966v1 | Accuracy | 95.7±0.9 |
Scene Text Recognition | SVTP | CCD-ViT-Small | https://arxiv.org/abs/2211.00288v4 | Accuracy | 92.7 |
Scene Text Recognition | SVTP | CCD-ViT-Tiny | https://arxiv.org/abs/2211.00288v4 | Accuracy | 91.6 |
Scene Text Recognition | SVTP | S-GTR | https://arxiv.org/abs/2112.12916v1 | Accuracy | 90.6 |
Scene Text Recognition | SVTP | MATRN | https://arxiv.org/abs/2111.15263v3 | Accuracy | 90.6 |
Scene Text Recognition | SVTP | SIGA_T | https://arxiv.org/abs/2203.03382v4 | Accuracy | 90.5 |
Scene Text Recognition | SVTP | CDistNet (Ours) | https://arxiv.org/abs/2111.11011v5 | Accuracy | 89.77 |
Scene Text Recognition | SVTP | DiffusionSTR | https://arxiv.org/abs/2306.16707v1 | Accuracy | 89.2 |
Scene Text Recognition | SVTP | DPAN | https://dl.acm.org/doi/10.1145/3460426.3463674 | Accuracy | 89.0 |
Scene Text Recognition | CUTE80 | CPPD | https://arxiv.org/abs/2307.12270v2 | Accuracy | 99.7 |
Scene Text Recognition | CUTE80 | CLIP4STR-L (DataComp-1B) | https://arxiv.org/abs/2305.14014v4 | Accuracy | 99.7 |
Scene Text Recognition | CUTE80 | CLIP4STR-B* | https://arxiv.org/abs/2401.00028v3 | Accuracy | 99.65 |
Scene Text Recognition | CUTE80 | MGP-STR | https://arxiv.org/abs/2209.03592v2 | Accuracy | 99.31 |
Scene Text Recognition | CUTE80 | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | Accuracy | 99.3 |
Scene Text Recognition | CUTE80 | DTrOCR 105M | https://arxiv.org/abs/2308.15996v1 | Accuracy | 99.1 |
Scene Text Recognition | CUTE80 | CLIP4STR-L | https://arxiv.org/abs/2305.14014v4 | Accuracy | 99.0 |
Scene Text Recognition | CUTE80 | PARSeq | https://arxiv.org/abs/2207.06966v1 | Accuracy | 98.3±0.6 |
Scene Text Recognition | CUTE80 | CCD-ViT-Small(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 98.3 |
Scene Text Recognition | CUTE80 | CCD-ViT-Base(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 98.3 |
Scene Text Recognition | CUTE80 | CCD-ViT-Tiny(ARD_2.8M) | https://arxiv.org/abs/2211.00288v4 | Accuracy | 95.8 |
Scene Text Recognition | CUTE80 | S-GTR | https://arxiv.org/abs/2112.12916v1 | Accuracy | 94.7 |
Scene Text Recognition | CUTE80 | MATRN | https://arxiv.org/abs/2111.15263v3 | Accuracy | 93.5 |
Scene Text Recognition | CUTE80 | SIGA_T | https://arxiv.org/abs/2203.03382v4 | Accuracy | 93.1 |
Scene Text Recognition | CUTE80 | DiffusionSTR | https://arxiv.org/abs/2306.16707v1 | Accuracy | 92.5 |
Scene Text Recognition | CUTE80 | NRTR+TPS++ | https://arxiv.org/abs/2305.05322v1 | Accuracy | 92.4 |
Scene Text Recognition | CUTE80 | DPAN | https://dl.acm.org/doi/10.1145/3460426.3463674 | Accuracy | 91.9 |
Scene Text Recognition | CUTE80 | CDistNet (Ours) | https://arxiv.org/abs/2111.11011v5 | Accuracy | 89.58 |
Scene Text Recognition | Uber-Text | CLIP4STR-L (DataComp-1B) | https://arxiv.org/abs/2305.14014v4 | Accuracy (%) | 92.2 |
Scene Text Recognition | Uber-Text | MGP-STR | https://arxiv.org/abs/2209.03592v2 | Accuracy (%) | 91.0 |
Scene Text Recognition | Uber-Text | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | Accuracy (%) | 86.8 |
Scene Text Recognition | WOST | CLIP4STR-H (DFN-5B) | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 90.9 |
Scene Text Recognition | WOST | CLIP4STR-L (DataComp-1B) | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 90.6 |
Scene Text Recognition | WOST | CLIP4STR-L | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 88.8 |
Scene Text Recognition | WOST | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 87.0 |
Scene Text Recognition | WOST | CCD-ViT-Base | https://arxiv.org/abs/2211.00288v4 | 1:1 Accuracy | 86.0 |
Scene Text Recognition | SVT-P | ABINet-LV+TPS++ | https://arxiv.org/abs/2305.05322v1 | Accuracy | 89.6 |
Scene Text Recognition | COCO-Text | CLIP4STR-L | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 81.9 |
Scene Text Recognition | COCO-Text | MGP-STR | https://arxiv.org/abs/2209.03592v2 | 1:1 Accuracy | 81.7 |
Scene Text Recognition | COCO-Text | CLIP4STR-B | https://arxiv.org/abs/2305.14014v4 | 1:1 Accuracy | 81.1 |
Scene Text Recognition | COCO-Text | PARSeq | https://arxiv.org/abs/2207.06966v1 | 1:1 Accuracy | 79.8±0.1 |
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