When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
<|reference_start|>When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective: Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can significantly improve the performance of out-of-distribution tasks. On the other hand, linear probing, a standard transfer learning method, can sometimes become the best approach. We propose a log-likelihood ratio (LLR) approach to analyze the comparative benefits of visual prompting and linear probing. By employing the LLR score alongside resource-efficient visual prompts approximations, our cost-effective measure attains up to a 100-fold reduction in run time compared to full training, while achieving prediction accuracies up to 91%. The source code is available at https://github.com/IBM/VP-LLR.<|reference_end|>
@article{tsao2024when,
    title={When Does Visual Prompting Outperform Linear Probing for Vision-Language
  Models? A Likelihood Perspective},
    author={Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Tsung-Yi Ho},
    journal={arXiv preprint arXiv:2409.01821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01821},
    primaryClass={cs.CV cs.LG}
}