Explainable AI is important for improving transparency, accountability, trust, and ethical
considerations in AI systems, and for enabling users to make informed decisions based on …

The transferability of adversarial examples is a crucial aspect of evaluating the robustness of
deep learning systems, particularly in black-box scenarios. Although several methods have …

The development of large language models (LLMs) has significantly advanced the
emergence of large multimodal models (LMMs). While LMMs have achieved tremendous …

Recent advances in Large Vision-Language Models (LVLMs) have significantly improve
performance in image comprehension tasks, such as formatted charts and rich-content …

Despite achieving outstanding performance on various cross-modal tasks, current large
vision-language models (LVLMs) still suffer from hallucination issues, manifesting as …

In the digital age, determining the authenticity of images has become increasingly crucial.
This study aims to explore the capability of machine learning models in identifying …

While deep neural networks (DNNs) achieve impressive performance on environment
perception tasks, their sensitivity to adversarial perturbations limits their use in practical …

Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in
generalizing across a diverse set of multi-modal tasks, especially for Visual Question …

Despite achieving rapid developments and with widespread applications, Large Vision-
Language Models (LVLMs) confront a serious challenge of being prone to generating …

Large Vision Language Models exhibit remarkable capabilities but struggle with
hallucinations inconsistencies between images and their descriptions. Previous …