Connecting the Dots: Collaborative Fine-tuning for Black-Box
  Vision-Language Models
Zhengbo Wang Jian Liang Ran He Zilei Wang Tieniu Tan
With the emergence of pretrained vision-language models (VLMs), considerable
efforts have been devoted to fine-tuning them for downstream tasks. Despite the
progress made in designing efficient fine-tuning methods, such methods require
access to the model's parameters, which can be challenging as model owners
often opt to provide their models as a black box to safeguard model ownership.
This paper proposes a \textbf{C}ollabo\textbf{ra}tive
\textbf{F}ine-\textbf{T}uning (\textbf{CraFT}) approach for fine-tuning
black-box VLMs to downstream tasks, where one only has access to the input
prompts and the output predictions of the model. CraFT comprises two modules, a
prompt generation module for learning text prompts and a prediction refinement
module for enhancing output predictions in residual style. Additionally, we
introduce an auxiliary prediction-consistent loss to promote consistent
optimization across these modules. These modules are optimized by a novel
collaborative training algorithm. Extensive experiments on few-shot
classification over 15 datasets demonstrate the superiority of CraFT. The
results show that CraFT achieves a decent gain of about 12\% with 16-shot
datasets and only 8,000 queries. Moreover, CraFT trains faster and uses only
about 1/80 of the memory footprint for deployment, while sacrificing only
1.62\% compared to the white-box method.