4 papers
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
Ruijiang Dong, Zesheng Ye, Jianzhong Qi +4
Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by ins…
Green Shielding: A User-Centric Approach Towards Trustworthy AI
Aaron J. Li, Nicolas Sanchez, Hao Huang +8
Large language models (LLMs) are increasingly deployed, yet their outputs can be highly sensitive to routine, non-adversarial variation in how users phrase queries, a gap not well…
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
Zesheng Ye, Chengyi Cai, Ruijiang Dong +4
As large-scale pre-trained foundation models continue to expand in size and capability, efficiently adapting them to specific downstream tasks has become increasingly critical. Des…
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Ruijiang Dong, Feng Liu, Haoang Chi +5
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a…