---
license: apache-2.0
datasets:
- mikewang/PVD-160K
---

<h1 align="center"> Text-Based Reasoning About Vector Graphics </h1>

<p align="center">
<a href="https://mikewangwzhl.github.io/VDLM">🌐 Homepage</a>
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<a href="https://arxiv.org/abs/2404.06479">📃 Paper</a>
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<a href="https://huggingface.co/datasets/mikewang/PVD-160K" >🤗 Data (PVD-160k)</a>
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<a href="https://huggingface.co/mikewang/PVD-160k-Mistral-7b" >🤗 Model (PVD-160k-Mistral-7b)</a>
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<a href="https://github.com/MikeWangWZHL/VDLM" >💻 Code</a>

</p>


We observe that current *large multimodal models (LMMs)* still struggle with seemingly straightforward reasoning tasks that require precise perception of low-level visual details, such as identifying spatial relations or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics—images composed purely of 2D objects and shapes.

![Teaser](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/teaser.png?raw=true)

To solve this challenge, we propose **Visually Descriptive Language Model (VDLM)**, a text-based visual reasoning framework for vector graphics. VDLM operates on text-based visual descriptions—specifically, SVG representations and learned Primal Visual Descriptions (PVD), enabling zero-shot reasoning with an off-the-shelf LLM. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our [paper (coming soon)]() for more details.

![Overview](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/overview.png?raw=true)