The evaluation of visual hallucinations in multimodal AI models is novel and significant
because it addresses a critical gap in understanding how AI systems interpret deceptive …

Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how
machines understand and generate textual responses based on visual inputs. Despite their …

Despite the promising progress in multi-modal tasks, current large multi-modal models
(LMMs) are prone to hallucinating inconsistent descriptions with respect to the associated …

Recent advancements in vision-enabled large language models have prompted a renewed
interest in evaluating their capabilities and limitations when interpreting complex visual data …

The evolution of Artificial Intelligence Generated Contents (AIGCs) is advancing towards
higher quality. The growing interactions with AIGCs present a new challenge to the data …

While GPT-4V (ision) impressively models both visual and textual information
simultaneously, it's hallucination behavior has not been systematically assessed. To bridge …

Multi-modal Large Language Models (MLLMs) demonstrate remarkable success across
various vision-language tasks. However, they suffer from visual hallucination, where the …

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 …

Interpreting deep learning (DL) models is difficult due to the complexity of their internal
representations. Given the inherent lack of interpretability, it is challenging to identify the …

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 …