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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- pixel-level caption |
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- relationship analysis |
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- summarization |
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- question-answering |
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language: |
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- en |
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--- |
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## 🎨 Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want |
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The interaction between humans and artificial intelligence (AI) is a crucial factor that reflects the effectiveness of multimodal large language models (MLLMs). However, current MLLMs primarily focus on image-level comprehension and limit interaction to textual instructions, thereby constraining their flexibility in usage and depth of response. Therefore, we introduce the **Draw-and-Understand project**: a new model, a multi-domain dataset, and a challenging benchmark for visual prompting. |
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### Training and Eval Dataset Card |
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- MDVP-Data is a comprehensive dataset for multi-domain visual-prompt instruction tuning. This dataset encompasses data for both point-level and region-level understanding, designed to enhance a model’s comprehension ability and robustness. |
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- Based on MDVP-Data, we also introduce MDVP-Bench, a challenging benchmark designed to evaluate tasks that require a combination of detailed description referrals, inter-relationship analysis, and complex reasoning. |
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### Paper and Code |
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Paper: [https://arxiv.org/abs/2312.10032](https://arxiv.org/abs/2312.10032) \ |
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Code: [https://github.com/AFeng-x/Draw-and-Understand](https://github.com/AFeng-x/Draw-and-Understand) |
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### License |
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Attribution-NonCommercial 4.0 International \ |
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It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use. |
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### Citations |
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``` |
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@misc{ |
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} |
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``` |
