  Parameter-efficient fine-tuning (PEFT) methods have provided an effective way
for adapting large vision-language models to specific tasks or scenarios.
Typically, they learn a very small scale of parameters for pre-trained models
in a white-box formulation, which assumes model architectures to be known and
parameters to be accessible. However, large models are often not open-source
due to considerations of preventing abuse or commercial factors, hence posing a
barrier to the deployment of white-box PEFT methods. To alleviate the
dependence on model accessibility, we introduce collaborative black-box tuning
(CBBT) for both textual prompt optimization and output feature adaptation for
black-box models. Specifically, considering that the backpropagation gradients
are blocked, we approximate the gradients of textual prompts by analyzing the
predictions with perturbed prompts. Secondly, a lightweight adapter is deployed
over the output feature of the inaccessible model, further facilitating the
model adaptation process. Empowered with these designs, our CBBT is extensively
evaluated on eleven downstream benchmarks and achieves remarkable improvements
compared to existing black-box VL adaptation methods. Code is released at
https://github.com/guozix/cbbt.
