5 papers · 1 filter
Detail++: Training-Free Detail Enhancer for T2I Diffusion Models
Lifeng Chen, Jiner Wang, Zihao Pan +3
Recent advances in text-to-image (T2I) generation have led to impressive visual results. However, these models still face significant challenges when handling complex prompt, parti…
MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence
Hanqi Jiang, Junhao Chen, Mingyu Kang +12
Medical vision--language models (VLMs) are usually evaluated on intact image--question pairs, but trustworthy clinical use requires a stronger property: a model must recognise when…
Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation
Jiawei Mao, Yuhan Wang, Lifeng Chen +6
Recent advances in generative medical models are constrained by modality-specific scenarios that hinder the integration of complementary evidence from imaging, pathology, and clini…
Blind Spot Navigation: Evolutionary Discovery of Sensitive Semantic Concepts for LVLMs
Zihao Pan, Yu Tong, Weibin Wu +6
Adversarial attacks aim to generate malicious inputs that mislead deep models, but beyond causing model failure, they cannot provide certain interpretable information such as ``\te…
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion
Zihao Pan, Lifeng Chen, Weibin Wu +2
Systems based on deep neural networks are vulnerable to adversarial attacks. Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or…