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 ⌀ |
|---|---|---|---|---|---|
Conversational Web Navigation | WebLINX | Flan-T5-780M | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 15.36 |
Conversational Web Navigation | WebLINX | Flan-T5-780M | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 14.05 |
Conversational Web Navigation | WebLINX | Pix2Act-1.3B | https://arxiv.org/abs/2402.05930v2 | Overall score | 16.88 |
Conversational Web Navigation | WebLINX | Pix2Act-1.3B | https://arxiv.org/abs/2402.05930v2 | Intent Match | 81.80 |
Conversational Web Navigation | WebLINX | Pix2Act-1.3B | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 8.28 |
Conversational Web Navigation | WebLINX | Pix2Act-1.3B | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 25.21 |
Conversational Web Navigation | WebLINX | MindAct-780M | https://arxiv.org/abs/2402.05930v2 | Overall score | 15.13 |
Conversational Web Navigation | WebLINX | MindAct-780M | https://arxiv.org/abs/2402.05930v2 | Intent Match | 75.87 |
Conversational Web Navigation | WebLINX | MindAct-780M | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 13.39 |
Conversational Web Navigation | WebLINX | MindAct-780M | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 13.58 |
Conversational Web Navigation | WebLINX | Flan-T5-250M | https://arxiv.org/abs/2402.05930v2 | Overall score | 14.99 |
Conversational Web Navigation | WebLINX | Flan-T5-250M | https://arxiv.org/abs/2402.05930v2 | Intent Match | 79.69 |
Conversational Web Navigation | WebLINX | Flan-T5-250M | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 14.86 |
Conversational Web Navigation | WebLINX | Flan-T5-250M | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 9.21 |
Conversational Web Navigation | WebLINX | MindAct-250M | https://arxiv.org/abs/2402.05930v2 | Overall score | 12.63 |
Conversational Web Navigation | WebLINX | MindAct-250M | https://arxiv.org/abs/2402.05930v2 | Intent Match | 74.25 |
Conversational Web Navigation | WebLINX | MindAct-250M | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 12.05 |
Conversational Web Navigation | WebLINX | MindAct-250M | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 7.67 |
Conversational Web Navigation | WebLINX | Pix2Act-282M | https://arxiv.org/abs/2402.05930v2 | Overall score | 12.51 |
Conversational Web Navigation | WebLINX | Pix2Act-282M | https://arxiv.org/abs/2402.05930v2 | Intent Match | 79.71 |
Conversational Web Navigation | WebLINX | Pix2Act-282M | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 6.20 |
Conversational Web Navigation | WebLINX | Pix2Act-282M | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 16.40 |
Conversational Web Navigation | WebLINX | GPT-4T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Overall score | 10.72 |
Conversational Web Navigation | WebLINX | GPT-4T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Intent Match | 41.66 |
Conversational Web Navigation | WebLINX | GPT-4T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 10.85 |
Conversational Web Navigation | WebLINX | GPT-4T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 6.75 |
Conversational Web Navigation | WebLINX | GPT-4V (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Overall score | 10.45 |
Conversational Web Navigation | WebLINX | GPT-4V (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Intent Match | 42.36 |
Conversational Web Navigation | WebLINX | GPT-4V (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 10.91 |
Conversational Web Navigation | WebLINX | GPT-4V (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 6.21 |
Conversational Web Navigation | WebLINX | GPT-3.5T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Overall score | 8.51 |
Conversational Web Navigation | WebLINX | GPT-3.5T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Intent Match | 42.77 |
Conversational Web Navigation | WebLINX | GPT-3.5T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Element (IoU) | 8.62 |
Conversational Web Navigation | WebLINX | GPT-3.5T (Zero-Shot) | https://arxiv.org/abs/2402.05930v2 | Text (F1) | 3.45 |
Distance regression | CHILI-3K | EdgeCNN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.015 +/- 0.001 |
Distance regression | CHILI-3K | GraphSAGE | https://arxiv.org/abs/2402.13221v2 | MSE | 0.055 +/- 0.002 |
Distance regression | CHILI-3K | GraphUNet | https://arxiv.org/abs/2402.13221v2 | MSE | 0.055 +/- 0.001 |
Distance regression | CHILI-3K | GCN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.056 +/- 0.006 |
Distance regression | CHILI-3K | Mean | https://arxiv.org/abs/2402.13221v2 | MSE | 0.265 |
Distance regression | CHILI-3K | GAT | https://arxiv.org/abs/2402.13221v2 | MSE | 0.342 +/- 0.117 |
Distance regression | CHILI-3K | PMLP | https://arxiv.org/abs/2402.13221v2 | MSE | 0.359 +/- 0.017 |
Distance regression | CHILI-3K | GIN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.464 +/- 0.005 |
Distance regression | CHILI-100K | EdgeCNN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.030 +/- 0.001 |
Distance regression | CHILI-100K | GraphSAGE | https://arxiv.org/abs/2402.13221v2 | MSE | 0.064 +/- 0.001 |
Distance regression | CHILI-100K | GraphUNet | https://arxiv.org/abs/2402.13221v2 | MSE | 0.085 +/- 0.002 |
Distance regression | CHILI-100K | GCN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.090 +/- 0.002 |
Distance regression | CHILI-100K | GAT | https://arxiv.org/abs/2402.13221v2 | MSE | 0.252 +/- 0.003 |
Distance regression | CHILI-100K | Mean | https://arxiv.org/abs/2402.13221v2 | MSE | 0.307 |
Distance regression | CHILI-100K | PMLP | https://arxiv.org/abs/2402.13221v2 | MSE | 0.486 +/- 0.014 |
Distance regression | CHILI-100K | GIN | https://arxiv.org/abs/2402.13221v2 | MSE | 0.491 +/- 0.038 |
video narration captioning | Shot2Story20K | Ours | https://arxiv.org/abs/2312.10300v2 | METEOR | 24.8 |
video narration captioning | Shot2Story20K | Ours | https://arxiv.org/abs/2312.10300v2 | ROUGE | 39 |
video narration captioning | Shot2Story20K | Ours | https://arxiv.org/abs/2312.10300v2 | BLEU-4 | 18.8 |
video narration captioning | Shot2Story20K | Ours | https://arxiv.org/abs/2312.10300v2 | CIDEr | 168.7 |
Few Shot Open Set Object Detection | MSCOCO | FOODv2 | https://dl.acm.org/doi/10.1145/3581783.3611850 | AR_U | 16.52 |
Computational Efficiency | Plant village | ViTaL | https://arxiv.org/abs/2402.17424v2 | Hamming Loss | 0.054 |
Solar Irradiance Forecasting | ASOS Data | MST-GCN | https://www.mdpi.com/1424-8220/22/19/7179 | Accuracy | 0.79 |
Solar Irradiance Forecasting | ASOS Data | MST-GCN | https://www.mdpi.com/1424-8220/22/19/7179 | R^2 | 0.94 |
Solar Irradiance Forecasting | ASOS Data | MST-GCN | https://www.mdpi.com/1424-8220/22/19/7179 | Variance | 0.94 |
Solar Irradiance Forecasting | ASOS Data | MST-GCN | https://www.mdpi.com/1424-8220/22/19/7179 | MSE | 0.23 |
Solar Irradiance Forecasting | ASOS Data | MST-GCN | https://www.mdpi.com/1424-8220/22/19/7179 | MAE | 0.12 |
Visual Question Answering | VQA v2 test-dev | BLIP-2 ViT-G OPT 6.7B (fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Accuracy | 82.30 |
Visual Question Answering | VQA v2 test-dev | CoCa | https://arxiv.org/abs/2205.01917v2 | Accuracy | 82.3 |
Visual Question Answering | VQA v2 test-dev | OFA | https://arxiv.org/abs/2202.03052v2 | Accuracy | 82.0 |
Visual Question Answering | VQA v2 test-dev | BLIP-2 ViT-G OPT 2.7B (fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Accuracy | 81.74 |
Visual Question Answering | VQA v2 test-dev | BLIP-2 ViT-G FlanT5 XL (fine-tuned) | https://arxiv.org/abs/2301.12597v3 | Accuracy | 81.66 |
Visual Question Answering | VQA v2 test-dev | mPLUG-2 | https://arxiv.org/abs/2302.00402v1 | Accuracy | 81.11 |
Visual Question Answering | VQA v2 test-dev | Florence | https://arxiv.org/abs/2111.11432v1 | Accuracy | 80.16 |
Visual Question Answering | VQA v2 test-dev | Aurora (ours, r=64) | null | Accuracy | 77.69 |
Visual Question Answering | VQA v2 test-dev | VK-OOD | https://arxiv.org/abs/2302.05608v1 | Accuracy | 76.8 |
Visual Question Answering | VQA v2 test-dev | LXMERT (low-magnitude pruning) | https://arxiv.org/abs/2310.15325v1 | Accuracy | 70.72 |
Visual Question Answering | VQA v2 test-dev | LocVLM-L | https://arxiv.org/abs/2404.07449v1 | Accuracy | 56.2 |
Visual Question Answering | MMBench | LLaVA-InternLM2-ViT + MoSLoRA | https://arxiv.org/abs/2406.11909v3 | GPT-3.5 score | 73.8 |
Visual Question Answering | MMBench | CuMo-7B | https://arxiv.org/abs/2405.05949v1 | GPT-3.5 score | 73.0 |
Visual Question Answering | MMBench | LLaVA-LLaMA3-8B-ViT + MoSLoRA | https://arxiv.org/abs/2406.11909v3 | GPT-3.5 score | 73.0 |
Visual Question Answering | MMBench | Video-LaVIT | https://arxiv.org/abs/2402.03161v3 | GPT-3.5 score | 67.3 |
Visual Question Answering | MMBench | DreamLLM-7B | https://arxiv.org/abs/2309.11499v2 | GPT-3.5 score | 49.9 |
Visual Question Answering | CLEVR | NeSyCoCo Neuro-Symbolic | https://arxiv.org/abs/2412.15588v1 | Accuracy | 99.7 |
Visual Question Answering | MSVD-QA | FrozenBiLM | https://arxiv.org/abs/2206.08155v2 | Accuracy | 0.548 |
Visual Question Answering | MSVD-QA | Just Ask | https://arxiv.org/abs/2012.00451v3 | Accuracy | 0.463 |
Visual Question Answering | PlotQA-D2 | MatCha4096 + LaMenDa | http://openaccess.thecvf.com//content/CVPR2024/html/Li_Synthesize_Step-by-Step_Tools_Templates_and_LLMs_as_Data_Generators_for_CVPR_2024_paper.html | 1:1 Accuracy | 91.84 |
Visual Question Answering | PlotQA-D2 | MatCha | https://arxiv.org/abs/2212.09662v2 | 1:1 Accuracy | 90.7 |
Visual Question Answering | MM-Vet (w/o External Tools) | Emu-14B | https://arxiv.org/abs/2307.05222v2 | GPT-4 score | 36.3±0.3 |
Visual Question Answering | MM-Vet | gemini-2.0-flash-exp | null | GPT-4 score | 81.2±0.4 |
Visual Question Answering | MM-Vet | gemini-exp-1206 | null | GPT-4 score | 78.1±0.2 |
Visual Question Answering | MM-Vet | Gemini 1.5 Pro (gemini-1.5-pro-002) | https://arxiv.org/abs/2403.05530v5 | GPT-4 score | 76.9±0.1 |
Visual Question Answering | MM-Vet | MMCTAgent (GPT-4 + GPT-4V) | https://arxiv.org/abs/2405.18358v1 | GPT-4 score | 74.24 |
Visual Question Answering | MM-Vet | Claude 3.5 Sonnet (claude-3-5-sonnet-20240620) | https://www-cdn.anthropic.com/fed9cc193a14b84131812372d8d5857f8f304c52/Model_Card_Claude_3_Addendum.pdf | GPT-4 score | 74.2±0.2 |
Visual Question Answering | MM-Vet | Qwen2-VL-72B | https://arxiv.org/abs/2409.12191v2 | GPT-4 score | 74.0 |
Visual Question Answering | MM-Vet | InternVL2.5-78B | https://arxiv.org/abs/2412.05271v4 | GPT-4 score | 72.3 |
Visual Question Answering | MM-Vet | InternVL2.5-78B | https://arxiv.org/abs/2412.05271v4 | Params | 78B |
Visual Question Answering | MM-Vet | GPT-4o +text rationale +IoT | https://arxiv.org/abs/2405.13872v2 | GPT-4 score | 72.2 |
Visual Question Answering | MM-Vet | Lyra-Pro | https://arxiv.org/abs/2412.09501v1 | GPT-4 score | 71.4 |
Visual Question Answering | MM-Vet | Lyra-Pro | https://arxiv.org/abs/2412.09501v1 | Params | 74B |
Visual Question Answering | MM-Vet | GLM-4V-Plus | https://arxiv.org/abs/2408.16500v1 | GPT-4 score | 71.1 |
Visual Question Answering | MM-Vet | Phantom-7B | https://arxiv.org/abs/2409.14713v1 | GPT-4 score | 70.8 |
Visual Question Answering | MM-Vet | GPT-4o (gpt-4o-2024-05-13) | https://arxiv.org/abs/2303.08774v5 | GPT-4 score | 69.3±0.1 |
Visual Question Answering | MM-Vet | InternVL2.5-38B | https://arxiv.org/abs/2412.05271v4 | GPT-4 score | 68.8 |
Visual Question Answering | MM-Vet | InternVL2.5-38B | https://arxiv.org/abs/2412.05271v4 | Params | 38B |
Visual Question Answering | MM-Vet | gpt-4o-mini-2024-07-18 | https://arxiv.org/abs/2303.08774v5 | GPT-4 score | 68.6±0.1 |
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