Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation
This addresses the deployment barrier for parameter-efficient fine-tuning in black-box scenarios, though it is incremental as it builds on existing black-box adaptation methods.
The paper tackles the problem of adapting large vision-language models when they are not open-source by introducing collaborative black-box tuning (CBBT), which approximates gradients for prompt optimization and uses an adapter on output features, achieving significant improvements on eleven downstream benchmarks.
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.