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 ⌀ |
|---|---|---|---|---|---|
Image-to-Text Retrieval | Flickr30k | GSMN | https://arxiv.org/abs/2106.02400v1 | Recall@5 | 94.3 |
Image-to-Text Retrieval | Flickr30k | GSMN | https://arxiv.org/abs/2106.02400v1 | Recall@10 | 97.3 |
Image-to-Text Retrieval | Flickr30k | GSMN | https://arxiv.org/abs/2106.02400v1 | Recall@Sum | 268 |
Image-to-Text Retrieval | Flickr30k | LGSGM | https://arxiv.org/abs/2106.02400v1 | Recall@1 | 71 |
Image-to-Text Retrieval | Flickr30k | LGSGM | https://arxiv.org/abs/2106.02400v1 | Recall@5 | 91.9 |
Image-to-Text Retrieval | Flickr30k | LGSGM | https://arxiv.org/abs/2106.02400v1 | Recall@10 | 96.1 |
Image-to-Text Retrieval | Flickr30k | LGSGM | https://arxiv.org/abs/2106.02400v1 | Recall@Sum | 259 |
Image-to-Text Retrieval | FETA Car-Manuals | FETA's CLIP-MIL (Many-Shot Image-to-text) | https://arxiv.org/abs/2209.03648v2 | R@1 | 35.5 |
Image-to-Text Retrieval | FETA Car-Manuals | FETA's CLIP-MIL (Many-Shot Image-to-text) | https://arxiv.org/abs/2209.03648v2 | R@5 | 58.3 |
Image-to-Text Retrieval | FETA Car-Manuals | FETA's CLIP-MIL (Many-Shot Image-to-text) | https://arxiv.org/abs/2209.03648v2 | R@10 | 67 |
Image-to-Text Retrieval | WHOOPS! | BLIP2 FlanT5-XXL (Text-only FT) | https://arxiv.org/abs/2303.07274v4 | Specificity | 94 |
Image-to-Text Retrieval | WHOOPS! | BLIP2 FlanT5-XXL (Fine-tuned) | https://arxiv.org/abs/2303.07274v4 | Specificity | 84 |
Image-to-Text Retrieval | WHOOPS! | BLIP2 FlanT5-XL (Fine-tuned) | https://arxiv.org/abs/2303.07274v4 | Specificity | 81 |
Image-to-Text Retrieval | WHOOPS! | BLIP Large | https://arxiv.org/abs/2303.07274v4 | Specificity | 77 |
Image-to-Text Retrieval | WHOOPS! | CoCa ViT-L-14 MSCOCO | https://arxiv.org/abs/2303.07274v4 | Specificity | 72 |
Image-to-Text Retrieval | WHOOPS! | BLIP2 FlanT5-XXL (Zero-shot) | https://arxiv.org/abs/2303.07274v4 | Specificity | 71 |
Image-to-Text Retrieval | WHOOPS! | CLIP ViT-L/14 | https://arxiv.org/abs/2303.07274v4 | Specificity | 70 |
Image-to-Text Retrieval | COCO | SigLIP (ViT-L, zero-shot) | https://arxiv.org/abs/2303.15343v4 | Recall@1 | 70.6 |
Image-to-Text Retrieval | RSICD | GeoRSCLIP-FT | https://arxiv.org/abs/2306.11300v5 | Image to Text Recall@1 | 22.14% |
Image-to-Text Retrieval | RUC-CAS-WenLan | CMCL | https://arxiv.org/abs/2103.06561v6 | Recall@1 | 36.1 |
Image-to-Text Retrieval | RUC-CAS-WenLan | CMCL | https://arxiv.org/abs/2103.06561v6 | Recall@5 | 55.5 |
Image-to-Text Retrieval | RUC-CAS-WenLan | CMCL | https://arxiv.org/abs/2103.06561v6 | Recall@10 | 62.2 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-G, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@10 | 98.5 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-G, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@1 | 85.4 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-G, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@5 | 97.0 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | ONE-PEACE (ViT-G, w/o ranking) | https://arxiv.org/abs/2305.11172v1 | Recall@10 | 98.3 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | ONE-PEACE (ViT-G, w/o ranking) | https://arxiv.org/abs/2305.11172v1 | Recall@1 | 84.1 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | ONE-PEACE (ViT-G, w/o ranking) | https://arxiv.org/abs/2305.11172v1 | Recall@5 | 96.3 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-L, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@10 | 98.0 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-L, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@1 | 83.5 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | BLIP-2 (ViT-L, fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Recall@5 | 96.0 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | IAIS | https://arxiv.org/abs/2105.13868v2 | Recall@10 | 94.48 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | IAIS | https://arxiv.org/abs/2105.13868v2 | Recall@1 | 67.78 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | IAIS | https://arxiv.org/abs/2105.13868v2 | Recall@5 | 89.7 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | CLIP (zero-shot) | https://arxiv.org/abs/2103.00020v1 | Recall@10 | 88.1 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | CLIP (zero-shot) | https://arxiv.org/abs/2103.00020v1 | Recall@1 | 58.4 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | CLIP (zero-shot) | https://arxiv.org/abs/2103.00020v1 | Recall@5 | 81.5 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | FLAVA (ViT-B, zero-shot) | https://arxiv.org/abs/2112.04482v3 | Recall@1 | 42.74 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | FLAVA (ViT-B, zero-shot) | https://arxiv.org/abs/2112.04482v3 | Recall@5 | 76.76 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | Oscar | https://arxiv.org/abs/2004.06165v5 | Recall@10 | 99.8 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | Unicoder-VL | https://arxiv.org/abs/1908.06066v3 | Recall@10 | 97.2 |
Image-to-Text Retrieval | COCO (Common Objects in Context) | DVSA | http://arxiv.org/abs/1412.2306v2 | Recall@10 | 74.8 |
Clothing Attribute Recognition | Clothing Attributes Dataset | Label2Label | https://arxiv.org/abs/2207.08677v1 | Accuracy | 92.87 |
Clothing Attribute Recognition | Clothing Attributes Dataset | MG-CNN | http://arxiv.org/abs/1601.00400v1 | Accuracy | 92.82 |
Clothing Attribute Recognition | Clothing Attributes Dataset | RAL_GNN | http://openaccess.thecvf.com/content_ECCV_2018/html/Zihang_Meng_Efficient_Relative_Attribute_ECCV_2018_paper.html | Accuracy | 92.39 |
Clothing Attribute Recognition | Clothing Attributes Dataset | S-CNN | http://arxiv.org/abs/1601.00400v1 | Accuracy | 90.43 |
Kinship Verification | KinFaceW-I | H-RGN | https://arxiv.org/abs/2109.02219v1 | Mean Accuracy | 82.6 |
Kinship Verification | KinFaceW-I | DSMM | https://arxiv.org/abs/2103.15108v1 | Mean Accuracy | 82.4 |
Kinship Verification | KinFaceW-I | GKR | https://arxiv.org/abs/2004.10375v1 | Mean Accuracy | 79.2 |
Kinship Verification | KinFaceW-I | GA | http://openaccess.thecvf.com/content_cvpr_2014/html/Dehghan_Who_Do_I_2014_CVPR_paper.html | Mean Accuracy | 74.5 |
Kinship Verification | KinFaceW-I | MNRML | https://ieeexplore.ieee.org/document/6562692 | Mean Accuracy | 69.3 |
Kinship Verification | KinFaceW-II | DSMM | https://arxiv.org/abs/2103.15108v1 | Mean Accuracy | 93.0 |
Kinship Verification | KinFaceW-II | H-RGN | https://arxiv.org/abs/2109.02219v1 | Mean Accuracy | 91.8 |
Kinship Verification | KinFaceW-II | KKR | https://arxiv.org/abs/2004.10375v1 | Mean Accuracy | 90.6 |
Kinship Verification | KinFaceW-II | GA | http://openaccess.thecvf.com/content_cvpr_2014/html/Dehghan_Who_Do_I_2014_CVPR_paper.html | Mean Accuracy | 82.2 |
Kinship Verification | KinFaceW-II | MNRML | https://ieeexplore.ieee.org/document/6562692 | Mean Accuracy | 76.5 |
Paraphrase Generation | Quora Question Pairs | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | iBLEU | 18.42 |
Paraphrase Generation | Quora Question Pairs | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | BLEU | 33.11 |
Paraphrase Generation | Quora Question Pairs | Separator | https://arxiv.org/abs/2105.15053v1 | iBLEU | 5.84 |
Paraphrase Generation | Paralex | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | iBLEU | 24.93 |
Paraphrase Generation | Paralex | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | BLEU | 39.49 |
Paraphrase Generation | Paralex | Separator | https://arxiv.org/abs/2105.15053v1 | iBLEU | 14.84 |
Paraphrase Generation | MSCOCO | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | iBLEU | 19.04 |
Paraphrase Generation | MSCOCO | HRQ-VAE | https://arxiv.org/abs/2203.03463v2 | BLEU | 27.90 |
Early Classification | ECG200 | SOCN | https://dl.acm.org/doi/abs/10.1145/3631531 | Accuracy | 0.9 |
4D Panoptic Segmentation | SemanticKITTI | Mask4Former | https://arxiv.org/abs/2309.16133v2 | LSTQ | 68.4 |
4D Panoptic Segmentation | SemanticKITTI | Eq-4D-StOP | https://arxiv.org/abs/2303.15651v2 | LSTQ | 67.8 |
4D Panoptic Segmentation | SemanticKITTI | Mask4D | https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2023ral-meem.pdf | LSTQ | 64.3 |
4D Panoptic Segmentation | SemanticKITTI | 4D-StOP | https://arxiv.org/abs/2209.14858v1 | LSTQ | 63.9 |
4D Panoptic Segmentation | SemanticKITTI | CIA | https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2022ral.pdf | LSTQ | 63.1 |
4D Panoptic Segmentation | SemanticKITTI | 4D-DS-Net | https://arxiv.org/abs/2203.07186v1 | LSTQ | 62.3 |
4D Panoptic Segmentation | SemanticKITTI | 4D-PLS | https://arxiv.org/abs/2102.12472v2 | LSTQ | 56.9 |
Micro-gesture Recognition | iMiGUE | null | https://arxiv.org/abs/2307.10624v1 | Top 1 Accuracy | 64.12 |
Micro-gesture Recognition | iMiGUE | null | https://arxiv.org/abs/2307.10624v1 | Top 5 Accuracy | 91.1 |
Zero-shot Generalization | CALVIN | GR-MG | https://arxiv.org/abs/2408.14368v2 | Avg. sequence length | 4.04 |
Zero-shot Generalization | CALVIN | MoDE | https://arxiv.org/abs/2412.12953v1 | Avg. sequence length | 4.01 |
Zero-shot Generalization | CALVIN | RoboUniView | https://arxiv.org/abs/2406.18977v3 | Avg. sequence length | 3.647 |
Zero-shot Generalization | CALVIN | 3D Diffuser Actor | https://arxiv.org/abs/2402.10885 | Avg. sequence length | 3.27 |
Zero-shot Generalization | CALVIN | GR-1 | https://arxiv.org/abs/2312.13139v2 | Avg. sequence length | 3.06 |
Atomic number classification | CHILI-100K | EdgeCNN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.572 +/- 0.017 |
Atomic number classification | CHILI-100K | GIN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.336 +/- 0.005 |
Atomic number classification | CHILI-100K | GraphUNet | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.287 +/- 0.004 |
Atomic number classification | CHILI-100K | GCN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.275 +/- 0.002 |
Atomic number classification | CHILI-100K | GraphSAGE | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.195 +/- 0.007 |
Atomic number classification | CHILI-100K | Most Frequent Class | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.192 |
Atomic number classification | CHILI-100K | GAT | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.192 +/- 0.000 |
Atomic number classification | CHILI-100K | PMLP | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.191 +/- 0.000 |
Atomic number classification | CHILI-100K | Random | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.015 +/- 0.000 |
Atomic number classification | CHILI-3K | EdgeCNN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.632 +/- 0.009 |
Atomic number classification | CHILI-3K | GIN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.587 +/- 0.002 |
Atomic number classification | CHILI-3K | GraphUNet | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.552 +/- 0.079 |
Atomic number classification | CHILI-3K | GCN | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.496 +/- 0.001 |
Atomic number classification | CHILI-3K | GraphSAGE | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.491 +/- 0.004 |
Atomic number classification | CHILI-3K | Most Frequent Class | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.461 |
Atomic number classification | CHILI-3K | PMLP | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.461 +/- 0.000 |
Atomic number classification | CHILI-3K | GAT | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.461 +/- 0.000 |
Atomic number classification | CHILI-3K | Random | https://arxiv.org/abs/2402.13221v2 | F1-score (Weighted) | 0.016 +/- 0.000 |
Event-based Object Segmentation | MVSEC-SEG | EventSAM | https://arxiv.org/abs/2312.16222v1 | mIoU | 0.40 |
Event-based Object Segmentation | MVSEC-SEG | ETNet | http://openaccess.thecvf.com//content/ICCV2021/html/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper.html | mIoU | 0.37 |
Event-based Object Segmentation | MVSEC-SEG | E2VID | https://arxiv.org/abs/1906.07165v1 | mIoU | 0.35 |
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