When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective

• URL: http://arxiv.org/abs/2409.01821v2
• Date: Wed, 4 Sep 2024 12:58:11 GMT
• Title: When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
• Authors: Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Tsung-Yi Ho,
• Abstract summary: We propose a log-likelihood ratio (LLR) approach to analyze the comparative benefits of visual prompting and linear probing. Our measure attains up to a 100-fold reduction in run time compared to full training, while achieving prediction accuracies up to 91%.
• Score: 57.05315507519704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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