We propose a novel method that uses optimized visual noise to suppress object hallucinations in LVMs. By enhancing factual grounding and reducing parametric bias, our approach improves model reliability across 8 vision-language models.
A PyTorch-based repository designed to provide a comprehensive framework for adversarial training and robustness evaluation in visual tasks.
