Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable
Diffusion, less research attention is paid to its adversarial robustness. In this work, we study …

The vulnerability of deep neural networks to imperceptible adversarial perturbations has
attracted widespread attention. Inspired by the success of vision-language foundation …

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive
performance across various tasks and exhibit remarkable zero-shot generalization capability …

Abstract Large pre-trained Vision-Language Models (VLMs) like CLIP despite having
remarkable generalization ability are highly vulnerable to adversarial examples. This work …

Given the power of vision transformers a new learning paradigm pre-training and then
prompting makes it more efficient and effective to address downstream visual recognition …

Deep learning has presented remarkable progress in various tasks. Despite the excellent
performance, deep learning models remain not robust, especially to well-designed …

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting.
While visual prompts offer a lightweight method of input-space adaptation for large-scale …