Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning
  Instruction Using Language Model
Authors: name: Wenqi Zhang, name: Zhenglin Cheng, name: Yuanyu He, name: Mengna Wang, name: Yongliang Shen, name: Zeqi Tan, name: Guiyang Hou, name: Mingqian He, name: Yanna Ma, name: Weiming Lu, name: Yueting Zhuang

Although most current large multimodal models (LMMs) can already understand
photos of natural scenes and portraits, their understanding of abstract images,
e.g., charts, maps, or layouts, and visual reasoning capabilities remains quite
rudimentary. They often struggle with simple daily tasks, such as reading time
from a clock, understanding a flowchart, or planning a route using a road map.
In light of this, we design a multi-modal self-instruct, utilizing large
language models and their code capabilities to synthesize massive abstract
images and visual reasoning instructions across daily scenarios. Our strategy
effortlessly creates a multimodal benchmark with 11,193 instructions for eight
visual scenarios: charts, tables, simulated maps, dashboards, flowcharts,
relation graphs, floor plans, and visual puzzles. This benchmark,
constructed with simple lines and geometric elements, exposes the shortcomings
of most advanced LMMs like Claude-3.5-Sonnet and GPT-4o in abstract image
understanding, spatial relations reasoning, and visual element induction.
Besides, to verify the quality of our synthetic data, we fine-tune an LMM using
62,476 synthetic chart, table and road map instructions. The results
demonstrate improved chart understanding and map navigation performance, and
also demonstrate potential benefits for other visual reasoning tasks. Our code
is available at: https://github.com/zwq2018/Multi-modal-Self-instruct.