A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 72 of its 181 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
72 of 181 references.
References · 72
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2023Segment Anythingcited by 8,158
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
- 2023A Survey of Large Language Modelscited by 2,537
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
- 2021Transformers in Vision: A Surveycited by 1,009
- 2023A Survey on Multimodal Large Language Modelscited by 898
38 more
- 2020Self-supervised Learning: Generative or Contrastivecited by 693
- 2020Image Segmentation Using Deep Learning: A Surveycited by 659
- 2020A Survey on Visual Transformercited by 653
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
- 2018Object Detection with Deep Learning: A Reviewcited by 524
- 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
- 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 446
- 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 396
- 2018Deep Learning for Generic Object Detection: A Surveycited by 392
- 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 364
- 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 339
- 2020Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlookscited by 312
- 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
- 2021Graph Self-Supervised Learning: A Surveycited by 289
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 281
- 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 242
- 2023Nougat: Neural Optical Understanding for Academic Documentscited by 240
- 2023Class-Incremental Learning: A Surveycited by 209
- 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 205
- 2023A Survey on Video Diffusion Modelscited by 203
- 2023Multimodal Large Language Models: A Surveycited by 193
- 2020U-Net and its variants for medical image segmentation: theory and applicationscited by 169
- 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
- 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
- 2023End-to-End Speech Recognition: A Surveycited by 127
- 2022Towards Large-Scale Small Object Detection: Survey and Benchmarkscited by 119
- 2021A Survey of Visual Transformerscited by 117
- 2021Geometric Deep Learning on Molecular Representationscited by 113
- 20213D Object Detection for Autonomous Driving: A Surveycited by 106
- 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
- 2022Self-Supervised Learning for Recommender Systems: A Surveycited by 95
- 2021A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcited by 82
- 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
- 2022A Survey on Long-Tailed Visual Recognitioncited by 75
- 2023A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AIcited by 68
- 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
- 2020Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspectivecited by 58
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