Large vision-language models (LVLMs) have recently dramatically pushed the
state of the art in image captioning and many image understanding tasks (e.g.,
visual question answering). LVLMs, however, often \textit{hallucinate} and
produce captions that mention concepts that cannot be found in the image. These
hallucinations erode the trustworthiness of LVLMs and are arguably among the
main obstacles to their ubiquitous adoption. Recent work suggests that addition
of grounding objectives -- those that explicitly align image regions or objects
to text spans -- reduces the amount of LVLM hallucination. Although intuitive,
this claim is not empirically justified as the reduction effects have been
established, we argue, with flawed evaluation protocols that (i) rely on data
(i.e., MSCOCO) that has been extensively used in LVLM training and (ii) measure
hallucination via question answering rather than open-ended caption generation.
In this work, in contrast, we offer the first systematic analysis of the effect
of fine-grained object grounding on LVLM hallucination under an evaluation
protocol that more realistically captures LVLM hallucination in open
generation. Our extensive experiments over three backbone LLMs reveal that
grounding objectives have little to no effect on object hallucination in open
caption generation.

在 LVLM 的开放式字幕生成中，细粒度对象定位目标对对象形象幻觉的效果很小或没有效果。

目标引导是否真能减少大型视觉语言模型的幻觉？

Does Object Grounding Really Reduce Hallucination of Large  Vision-Language Models?

Large vision-language models (LVLMs) have shown remarkable abilities in
understanding visual information with human languages. However, LVLMs still
suffer from object hallucination, which is the problem of generating
descriptions that include objects that do not actually exist in the images.
This can negatively impact many vision-language tasks, such as visual
summarization and reasoning. To address this issue, we propose a simple yet
powerful algorithm, LVLM Hallucination Revisor (LURE), to post-hoc rectify
object hallucination in LVLMs by reconstructing less hallucinatory
descriptions. LURE is grounded in a rigorous statistical analysis of the key
factors underlying object hallucination, including co-occurrence (the frequent
appearance of certain objects alongside others in images), uncertainty (objects
with higher uncertainty during LVLM decoding), and object position
(hallucination often appears in the later part of the generated text). LURE can
also be seamlessly integrated with any LVLMs. We evaluate LURE on six
open-source LVLMs, achieving a 23% improvement in general object hallucination
evaluation metrics over the previous best approach. In both GPT and human
evaluations, LURE consistently ranks at the top. Our data and code are
available at this https URL

LVLM Hallucination Revisor (LURE) 是一种简单而强大的算法，通过重建较少产生幻觉的描述来修正 LVLMs 中的物体幻觉问题，从而提高视觉总结和推理等视觉语言任务的性能。