Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
Chinese
Size:
100K - 1M
ArXiv:
DOI:
License:
tianyu-z
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Commit
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75867b0
README.md
CHANGED
@@ -65,25 +65,33 @@ We found that OCR and text-based processing become ineffective in VCR as accurat
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- **Point of Contact:** [Tianyu Zhang](mailto:tianyu.zhang@mila.quebec)
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# Benchmark
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EM means `"Exact Match"` and Jaccard means `"Jaccard Similarity"`. The best in closed source and open source are highlighted in **bold**. Closed source models are evaluated based on [500 test samples](https://huggingface.co/collections/vcr-org/vcr-visual-caption-restoration-500-test-subsets-6667c9efd77c55f2363b34a1), while open source models are evaluated based on [5000 test samples](https://huggingface.co/collections/vcr-org/vcr-visual-caption-restoration-6661393b1761e2aff7b967b9).
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| Model | Size (unknown for closed source) | En Easy EM | En Easy Jaccard | En Hard EM | En Hard Jaccard | Zh Easy EM | Zh Easy Jaccard | Zh Hard EM | Zh Hard Jaccard |
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| Claude 3 Opus | - | 62.0 | 77.67 | 37.8 | 57.68 | 0.9 | 11.5 | 0.3 | 9.22 |
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| Claude 3.5 Sonnet | - | 63.85 | 74.65 | 41.74 | 56.15 | 1.0 | 7.54 | 0.2 | 4.0 |
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| GPT-4 Turbo | - | 78.74 | 88.54 | 45.15 | 65.72 | 0.2 | 8.42 | 0.0 | 8.58 |
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| GPT-4V | - | 52.04 | 65.36 | 25.83 | 44.63 | - | - | - | - |
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| GPT-4o | - | **91.55** | **96.44** | **73.2** | **86.17** | **14.87** | **39.05** | **2.2** | **22.72** |
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| Gemini 1.5 Pro | - | 62.73 | 77.71 | 28.07 | 51.9 | 1.1 | 11.1 | 0.7 | 11.82 |
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| Qwen-VL-Max | - | 76.8 | 85.71 | 41.65 | 61.18 | 6.34 | 13.45 | 0.89 | 5.4 |
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| Reka Core | - | 66.46 | 84.23 | 6.71 | 25.84 | 0.0 | 3.43 | 0.0 | 3.35 |
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| DeepSeek-VL | 1.3B | 23.04 | 46.84 | 0.16 | 11.89 | 0.0 | 6.56 | 0.0 | 6.46 |
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| DeepSeek-VL | 7B | 38.01 | 60.02 | 1.0 | 15.9 | 0.0 | 4.08 | 0.0 | 5.11 |
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| DocOwl-1.5-Omni | 8B | 0.84 | 13.34 | 0.04 | 7.76 | 0.0 | 1.14 | 0.0 | 1.37 |
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| Idefics2 | 8B | 15.75 | 31.97 | 0.65 | 9.93 | - | - | - | - |
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| InternLM-XComposer2-VL | 7B | 46.64 | 70.99 | 0.7 | 12.51 | 0.27 | 12.32 | 0.07 | 8.97 |
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| InternVL-V1.5 | 25.5B | 14.65 | 51.42 | 1.99 | 16.73 | 4.78 | 26.43 | 0.03 | 8.46 |
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| MiniCPM-V2.5 | 8B | 31.81 | 53.24 | 1.41 | 11.94 | 4.1 | 18.03 | 0.09 | 7.39 |
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| Monkey | 7B | 50.66 | 67.6 | 1.96 | 14.02 | 0.62 | 8.34 | 0.12 | 6.36 |
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| Qwen-VL | 7B | 49.71 | 69.94 | 2.0 | 15.04 | 0.04 | 1.5 | 0.01 | 1.17 |
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- **Point of Contact:** [Tianyu Zhang](mailto:tianyu.zhang@mila.quebec)
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# Benchmark
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EM means `"Exact Match"` and Jaccard means `"Jaccard Similarity"`. The best in closed source and open source are highlighted in **bold**. The second best are highlighted in *italic*. Closed source models are evaluated based on [500 test samples](https://huggingface.co/collections/vcr-org/vcr-visual-caption-restoration-500-test-subsets-6667c9efd77c55f2363b34a1), while open source models are evaluated based on [5000 test samples](https://huggingface.co/collections/vcr-org/vcr-visual-caption-restoration-6661393b1761e2aff7b967b9).
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| Model | Size (unknown for closed source) | En Easy EM | En Easy Jaccard | En Hard EM | En Hard Jaccard | Zh Easy EM | Zh Easy Jaccard | Zh Hard EM | Zh Hard Jaccard |
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| Claude 3 Opus | - | 62.0 | 77.67 | 37.8 | 57.68 | 0.9 | 11.5 | 0.3 | 9.22 |
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| Claude 3.5 Sonnet | - | 63.85 | 74.65 | 41.74 | 56.15 | 1.0 | 7.54 | 0.2 | 4.0 |
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| GPT-4 Turbo | - | *78.74* | *88.54* | *45.15* | *65.72* | 0.2 | 8.42 | 0.0 | *8.58* |
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| GPT-4V | - | 52.04 | 65.36 | 25.83 | 44.63 | - | - | - | - |
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| GPT-4o | - | **91.55** | **96.44** | **73.2** | **86.17** | **14.87** | **39.05** | **2.2** | **22.72** |
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| Gemini 1.5 Pro | - | 62.73 | 77.71 | 28.07 | 51.9 | 1.1 | 11.1 | 0.7 | 11.82 |
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| Qwen-VL-Max | - | 76.8 | 85.71 | 41.65 | 61.18 | *6.34* | *13.45* | *0.89* | 5.4 |
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| Reka Core | - | 66.46 | 84.23 | 6.71 | 25.84 | 0.0 | 3.43 | 0.0 | 3.35 |
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| Cambrian-1 | 34B | 79.69 | 89.27 | *27.20* | 50.04 | 0.03 | 1.27 | 0.00 | 1.37 |
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| Cambrian-1 | 13B | 49.35 | 65.11 | 8.37 | 29.12 | - | - | - | - |
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| Cambrian-1 | 8B | 71.13 | 83.68 | 13.78 | 35.78 | - | - | - | - |
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| CogVLM2 | 19B | *83.25* | *89.75* | **37.98** | **59.99** | 9.15 | 17.12 | 0.08 | 3.67 |
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| CogVLM2-Chinese | 19B | 79.90 | 87.42 | 25.13 | 48.76 | **33.24** | **57.57** | **1.34** | **17.35** |
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| DeepSeek-VL | 1.3B | 23.04 | 46.84 | 0.16 | 11.89 | 0.0 | 6.56 | 0.0 | 6.46 |
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| DeepSeek-VL | 7B | 38.01 | 60.02 | 1.0 | 15.9 | 0.0 | 4.08 | 0.0 | 5.11 |
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| DocOwl-1.5-Omni | 8B | 0.84 | 13.34 | 0.04 | 7.76 | 0.0 | 1.14 | 0.0 | 1.37 |
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| GLM-4v | 9B | 43.72 | 74.73 | 24.83 | *53.82* | *31.78* | *52.57* | *1.20* | *14.73* |
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| Idefics2 | 8B | 15.75 | 31.97 | 0.65 | 9.93 | - | - | - | - |
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| InternLM-XComposer2-VL | 7B | 46.64 | 70.99 | 0.7 | 12.51 | 0.27 | 12.32 | 0.07 | 8.97 |
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| InternLM-XComposer2-VL-4KHD | 7B | 5.32 | 22.14 | 0.21 | 9.52 | 0.46 | 12.31 | 0.05 | 7.67 |
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| InternLM-XComposer2.5-VL | 7B | 41.35 | 63.04 | 0.93 | 13.82 | 0.46 | 12.97 | 0.11 | 10.95 |
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| InternVL-V1.5 | 25.5B | 14.65 | 51.42 | 1.99 | 16.73 | 4.78 | 26.43 | 0.03 | 8.46 |
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| InternVL-V2 | 26B | 74.51 | 86.74 | 6.18 | 24.52 | 9.02 | 32.50 | 0.05 | 9.49 |
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| InternVL-V2 | 40B | **84.67** | **92.64** | 13.10 | 33.64 | 22.09 | 47.62 | 0.48 | 12.57 |
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| MiniCPM-V2.5 | 8B | 31.81 | 53.24 | 1.41 | 11.94 | 4.1 | 18.03 | 0.09 | 7.39 |
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| Monkey | 7B | 50.66 | 67.6 | 1.96 | 14.02 | 0.62 | 8.34 | 0.12 | 6.36 |
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| Qwen-VL | 7B | 49.71 | 69.94 | 2.0 | 15.04 | 0.04 | 1.5 | 0.01 | 1.17 |
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