The recent advances in diffusion models (DMs) have revolutionized the generation of
realistic and complex images. However, these models also introduce potential safety …

Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but
they also pose safety risks, such as the potential generation of harmful content and copyright …

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 …

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 …

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 …

With the rapid advancement of multimodal learning, pre-trained Vision-Language Models
(VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between …

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 …

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 …

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 …