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 …

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

Multi-modal foundation models like OpenFlamingo, LLaVA, and GPT-4 are increasingly
used for various real-world tasks. Prior work has shown that these models are highly …

Improving out-of-distribution (OOD) generalization during in-distribution (ID) adaptation is a
primary goal of robust fine-tuning of zero-shot models beyond naive fine-tuning. However …

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 …

We establish rigorous benchmarks for visual perception robustness. Synthetic images such
as ImageNet-C ImageNet-9 and Stylized ImageNet provide specific type of evaluation over …

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