29 citations · 62 across the 27 of their papers we have counts for
6 papers · 1 filter
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +1
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 s…
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
Zhiyuan He, Pin-Yu Chen, Tsung-Yi Ho
The rapid advances in generative AI models have empowered the creation of highly realistic images with arbitrary content, raising concerns about potential misuse and harm, such as…
AutoVP: An Automated Visual Prompting Framework and Benchmark
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +2
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,…
How to Backdoor Diffusion Models?
Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via prog…
CARBEN: Composite Adversarial Robustness Benchmark
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, mu…
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen +1
Model robustness against adversarial examples of single perturbation type such as the -norm has been widely studied, yet its generalization to more realistic scenarios in…