  Large Vision-Language Models (LVLMs) have advanced considerably, intertwining
visual recognition and language understanding to generate content that is not
only coherent but also contextually attuned. Despite their success, LVLMs still
suffer from the issue of object hallucinations, where models generate plausible
yet incorrect outputs that include objects that do not exist in the images. To
mitigate this issue, we introduce Visual Contrastive Decoding (VCD), a simple
and training-free method that contrasts output distributions derived from
original and distorted visual inputs. The proposed VCD effectively reduces the
over-reliance on statistical bias and unimodal priors, two essential causes of
object hallucinations. This adjustment ensures the generated content is closely
grounded to visual inputs, resulting in contextually accurate outputs. Our
experiments show that VCD, without either additional training or the usage of
external tools, significantly mitigates the object hallucination issue across
different LVLM families. Beyond mitigating object hallucinations, VCD also
excels in general LVLM benchmarks, highlighting its wide-ranging applicability.
