We revisit and advance visual prompting (VP), an input prompting technique for vision tasks.
VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the …

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the
existing mainstream approaches mostly require training or finetuning the entire weights of …

In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …

Adversarial training is widely used to make classifiers robust to a specific threat or
adversary, such as l_p-norm bounded perturbations of a given p-norm. However, existing …

In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed,
pre-trained model at test time. Compared to conventional adversarial defenses, VP allows …

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-
learning from a collection of pre-trained models without access to the training data. Existing …

Adversarial robustness studies the worst-case performance of a machine learning model to
ensure safety and reliability. With the proliferation of deep-learning-based technology, the …

Zeroth-order (ZO) optimization has become a popular technique for solving machine
learning (ML) problems when first-order (FO) information is difficult or impossible to obtain …

In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained
Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has …

When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …