Interactive Data Synthesis for Systematic Vision Adaptation via
  LLMs-AIGCs Collaboration
Qifan Yu Juncheng Li Wentao Ye Siliang Tang Yueting Zhuang
Recent text-to-image generation models have shown promising results in
generating high-fidelity photo-realistic images. In parallel, the problem of
data scarcity has brought a growing interest in employing AIGC technology for
high-quality data expansion. However, this paradigm requires well-designed
prompt engineering that cost-less data expansion and labeling remain
under-explored. Inspired by LLM's powerful capability in task guidance, we
propose a new paradigm of annotated data expansion named as ChatGenImage. The
core idea behind it is to leverage the complementary strengths of diverse
models to establish a highly effective and user-friendly pipeline for
interactive data augmentation. In this work, we extensively study how LLMs
communicate with AIGC model to achieve more controllable image generation and
make the first attempt to collaborate them for automatic data augmentation for
a variety of downstream tasks. Finally, we present fascinating results obtained
from our ChatGenImage framework and demonstrate the powerful potential of our
synthetic data for systematic vision adaptation. Our codes are available at
https://github.com/Yuqifan1117/Labal-Anything-Pipeline.