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Hsi-Ai Tsao

2 accepted papers

2025

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

ICASSP 2025accepted

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 transfe…

Cited by 0SourceScholar
2024

AutoVP: An Automated Visual Prompting Framework and Benchmark

ICLR 2024poster

Visual prompting (VP) is an emerging parameter-efficient fine-tuning approach to adapting pre-trained vision models to solve various downstream image-classification tasks. However, there has hitherto been little systematic study of the design space of VP and no clear benchmark for evaluating its per…