  Large Vision-Language Models (LVLMs) excel in integrating visual and
linguistic contexts to produce detailed content, facilitating applications such
as image captioning. However, using LVLMs to generate descriptions often faces
the challenge of object hallucination (OH), where the output text misrepresents
actual objects in the input image. While previous studies attribute the
occurrence of OH to the inclusion of more details, our study finds technical
flaws in existing metrics, leading to unreliable evaluations of models and
conclusions about OH. This has sparked a debate on the question: Do more
details always introduce more hallucinations in LVLM-based image captioning?
  In this paper, we address this debate by proposing a novel decoding strategy,
Differentiated Beam Decoding (DBD), along with a reliable new set of evaluation
metrics: CLIP-Precision, CLIP-Recall, and CLIP-F1. DBD decodes the wealth of
information hidden in visual input into distinct language representations
called unit facts in parallel. This decoding is achieved via a well-designed
differential score that guides the parallel search and candidate screening. The
selected unit facts are then aggregated to generate the final caption. Our
proposed metrics evaluate the comprehensiveness and accuracy of image captions
by comparing the embedding groups of ground-truth image regions and generated
text partitions. Extensive experiments on the Visual Genome dataset validate
the effectiveness of our approach, demonstrating that it produces detailed
descriptions while maintaining low hallucination levels.
