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---
license: cc-by-nc-4.0
task_categories:
- pixel-level caption
- relationship analysis
- summarization
- question-answering
language:
- en
---
## 🎨 Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
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.
### Training and Eval Dataset Card
- 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.
- 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.
### Paper and Code
Paper: [https://arxiv.org/abs/2312.10032](https://arxiv.org/abs/2312.10032) \
Code: [https://github.com/AFeng-x/Draw-and-Understand](https://github.com/AFeng-x/Draw-and-Understand)
### License
Attribution-NonCommercial 4.0 International \
It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
### Citations
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@misc{
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``` |