File size: 1,970 Bytes
77de45c 1f3f554 79c8632 1f3f554 79c8632 1f3f554 44a96c1 1f3f554 79c8632 946a72b 1f3f554 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 277884785
num_examples: 160000
download_size: 126665150
dataset_size: 277884785
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
<h1 align="center"> Text-Based Reasoning About Vector Graphics </h1>
<p align="center">
<a href="https://mikewangwzhl.github.io/VDLM">🌐 Homepage</a>
•
<a href="https://arxiv.org/abs/2404.06479">📃 Paper</a>
•
<a href="https://huggingface.co/datasets/mikewang/PVD-160K" >🤗 Data (PVD-160k)</a>
•
<a href="https://huggingface.co/mikewang/PVD-160k-Mistral-7b" >🤗 Model (PVD-160k-Mistral-7b)</a>
•
<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.
To solve this challenge, we propose **Visually Descriptive Language Model (VDLM)**, a visual reasoning framework that operates with intermediate text-based visual descriptions—SVG representations and learned Primal Visual Description, which can be directly integrated into existing LLMs and LMMs. 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](https://arxiv.org/abs/2404.06479) for more details.
 |