Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal
  Language Models
Authors: name: Yushi Hu, name: Weijia Shi, name: Xingyu Fu, name: Dan Roth, name: Mari Ostendorf, name: Luke Zettlemoyer, name: Noah A Smith, name: Ranjay Krishna

Humans draw to facilitate reasoning: we draw auxiliary lines when solving
geometry problems; we mark and circle when reasoning on maps; we use sketches
to amplify our ideas and relieve our limited-capacity working memory. However,
such actions are missing in current multimodal language models (LMs). Current
chain-of-thought and tool-use paradigms only use text as intermediate reasoning
steps. In this work, we introduce Sketchpad, a framework that gives multimodal
LMs a visual sketchpad and tools to draw on the sketchpad. The LM conducts
planning and reasoning according to the visual artifacts it has drawn.
Different from prior work, which uses text-to-image models to enable LMs to
draw, Sketchpad enables LMs to draw with lines, boxes, marks, etc., which is
closer to human sketching and better facilitates reasoning. Sketchpad can also
use specialist vision models during the sketching process (e.g., draw bounding
boxes with object detection models, draw masks with segmentation models), to
further enhance visual perception and reasoning. We experiment with a wide
range of math tasks (including geometry, functions, graphs, and chess) and
complex visual reasoning tasks. Sketchpad substantially improves performance on
all tasks over strong base models with no sketching, yielding an average gain
of 12.7% on math tasks, and 8.6% on vision tasks. GPT-4o with Sketchpad sets a
new state of the art on all tasks, including V*Bench (80.3%), BLINK spatial
reasoning (83.9%), and visual correspondence (80.8%). All codes and data are in
https://visualsketchpad.github.io/.