PICS：用于图像描述与搜索的流水线
PICS: Pipeline for Image Captioning and Search
AI总结：
本文提出PICS，一种利用大型语言模型自动化图像描述并集成情感分析以丰富元数据的流水线，旨在解决大规模图像库的组织与高效检索问题。
摘要：
数字图像数量的不断增长需要先进的系统来实现高效的分类和检索，这在数据库管理和信息检索方面提出了重大挑战。本文介绍了PICS（Pipeline for Image Captioning and Search，用于图像描述与搜索的流水线），这是一种旨在解决组织大规模图像库所固有的复杂性的新颖方法。PICS利用大型语言模型（LLMs）的进步来自动化图像描述过程，提供了一种超越传统手动标注方法的解决方案。该方法基于这样一种理解，即有意义的、AI生成的描述可以显著增强大型数据库中图像的可搜索性和可访问性。通过将情感分析集成到流水线中，PICS进一步丰富了元数据，使得能够进行超越基本描述符的细致搜索。这种方法不仅简化了管理大量图像集合的任务，而且在图像检索的准确性和效率方面树立了新的先例。PICS的意义在于其改变图像数据库系统的潜力，利用机器学习和自然语言处理的力量来满足现代数字资产管理的需求。
英文摘要：
The growing volume of digital images necessitates advanced systems for efficient categorization and retrieval, presenting a significant challenge in database management and information retrieval. This paper introduces PICS (Pipeline for Image Captioning and Search), a novel approach designed to address the complexities inherent in organizing large-scale image repositories. PICS leverages the advancements in Large Language Models (LLMs) to automate the process of image captioning, offering a solution that transcends traditional manual annotation methods. The approach is rooted in the understanding that meaningful, AI-generated captions can significantly enhance the searchability and accessibility of images in large databases. By integrating sentiment analysis into the pipeline, PICS further enriches the metadata, enabling nuanced searches that extend beyond basic descriptors. This methodology not only simplifies the task of managing vast image collections but also sets a new precedent for accuracy and efficiency in image retrieval. The significance of PICS lies in its potential to transform image database systems, harnessing the power of machine learning and natural language processing to meet the demands of modern digital asset management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
