Image description datasets play a crucial role in the advancement of various
applications such as image understanding, text-to-image generation, and
text-image retrieval. Currently, image description datasets primarily originate
from two sources. One source is the scraping of image-text pairs from the web.
Despite their abundance, these descriptions are often of low quality and noisy.
Another is through human labeling. Datasets such as COCO are generally very
short and lack details. Although detailed image descriptions can be annotated
by humans, the high annotation cost limits the feasibility. These limitations
underscore the need for more efficient and scalable methods to generate
accurate and detailed image descriptions. In this paper, we propose an
innovative framework termed Image Textualization (IT), which automatically
produces high-quality image descriptions by leveraging existing multi-modal
large language models (MLLMs) and multiple vision expert models in a
collaborative manner, which maximally convert the visual information into text.
To address the current lack of benchmarks for detailed descriptions, we propose
several benchmarks for comprehensive evaluation, which verifies the quality of
image descriptions created by our framework. Furthermore, we show that
LLaVA-7B, benefiting from training on IT-curated descriptions, acquire improved
capability to generate richer image descriptions, substantially increasing the
length and detail of their output with less hallucination.

通过结合现有的多模态大语言模型（MLLMs）与多个视觉专家模型，我们提出了一种名为图像文本化（IT）的创新框架，以最大程度地将视觉信息转化为文本，自动生成高质量的图像描述。我们还提出了几个综合评估基准来验证我们框架所创建的图像描述的质量，显示经过 IT 精选描述训练的 LLaVA-7B 模型在生成更丰富的图像描述方面的能力得到了提高。