upload

README.md

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 ---
 license: cc-by-nc-4.0
 task_categories:
-- pixel-level caption
-- relationship analysis
+- text-generation
+- image-to-text
 - summarization
 - question-answering
 language:
 - en
 ---
 
-## 🎨 Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
+# 🎨 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
+## 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 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
+## License
 Attribution-NonCommercial 4.0 International \
 It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
 
 
-### Citations
+## Citations
 ```
 @misc{
 }

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