license: apache-2.0
datasets:
- mikewang/PVD-160K
Text-Based Reasoning About Vector Graphics
🌐 Homepage • 📃 Paper • 🤗 Data (PVD-160k) • 🤗 Model (PVD-160k-Mistral-7b) • 💻 Code
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.
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.
