On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in
  Large Vision-Language Models
Authors: name: Hoigi Seo, name: Dong Un Kang, name: Hyunjin Cho, name: Joohoon Lee, name: Se Young Chun
Organization: Seoul National University

Large vision-language models (LVLMs), which integrate a vision encoder (VE)
with a large language model, have achieved remarkable success across various
tasks. However, there are still crucial challenges in LVLMs such as object
hallucination, generating descriptions of objects that are not in the input
image. Here, we argue that uncertain visual tokens within the VE is a key
factor that contributes to object hallucination. Our statistical analysis found
that there are positive correlations between visual tokens with high epistemic
uncertainty and the occurrence of hallucinations. Furthermore, we show
theoretically and empirically that visual tokens in early VE layers that
exhibit large representation deviations under small adversarial perturbations
indicate high epistemic uncertainty. Based on these findings, we propose a
simple yet effective strategy to mitigate object hallucination by modifying the
VE only. Our method comprises a proxy method with adversarial perturbations for
identifying uncertain visual tokens efficiently and a method to mask these
uncertain visual tokens during the self-attention process in the middle layers
of the VE, suppressing their influence on visual encoding and thus alleviating
hallucinations. Extensive experiments show that our method significantly
reduces object hallucinations in LVLMs and can synergistically work with other
prior arts.