模式分析与机器智能领域文献综述的文献综述
A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence
AI总结：
本研究对PAMI领域3000多篇综述进行三级分析，构建RiPAMI数据库，提出超越引用计数的指标引导导航策略，并评估AI生成综述的进展与不足，展望其作为传统综述的补充。
摘要：
模式分析与机器智能（PAMI）领域的快速研究增长使得文献综述对于整合和解读其众多子领域的知识变得至关重要。在本工作中，我们沿着三个互补维度对PAMI综述进行了全面的三级分析：（i）识别现有综述中结构和统计上的规律性；（ii）开发定量策略，帮助研究者在不断扩大的综述语料库中进行导航和优先级排序；以及（iii）批判性评估新兴的AI生成综述系统。为支持本研究，我们构建了RiPAMI，一个包含超过3000篇综述文章的大规模数据库，并将叙述性综合与统计分析相结合，以捕捉结构和内容层面的特征。我们的分析揭示了当前综述实践中独特的组织模式以及持续存在的空白。基于这些见解，我们提出了实用的、文章级别的指标引导导航策略，这些策略超越了简单的引用计数。最后，我们对最先进的AI生成综述的评估显示，在连贯性和组织性方面取得了令人鼓舞的进展，但也突显了在参考文献检索、近期工作覆盖以及视觉元素整合方面的持久弱点。综合来看，这些发现既对现有综述实践进行了批判性评估，也提供了前瞻性视角，展望AI生成综述如何演变为传统人工撰写综述的可信、可定制且具有变革性的补充。
英文摘要：
The rapid growth of research in Pattern Analysis and Machine Intelligence (PAMI) has rendered literature reviews essential for consolidating and interpreting knowledge across its many subfields. In this work, we present a comprehensive tertiary analysis of PAMI reviews along three complementary dimensions: (i) identifying structural and statistical regularities in existing surveys; (ii) developing quantitative strategies that help researchers navigate and prioritize within the expanding review corpus; and (iii) critically assessing emerging AI-generated review systems. To support this study, we construct RiPAMI, a large-scale database containing more than 3,000 review articles, and combine narrative synthesis with statistical analysis to capture structural and content-level features. Our analyses reveal distinctive organizational patterns as well as persistent gaps in current review practices. Building on these insights, we propose practical, article-level strategies for indicator-guided navigation that move beyond simple citation counts. Finally, our evaluation of state-of-the-art AI-generated reviews indicates encouraging advances in coherence and organization, yet also highlights enduring weaknesses in reference retrieval, coverage of recent work, and the incorporation of visual elements. Together, these findings provide both a critical appraisal of existing review practices and a forward-looking perspective on how AI-generated reviews can evolve into trustworthy, customizable, and transformative complements to traditional human-authored surveys.
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。
