mlsanityData to 14 Oct 2026

Foundation Models and Transformers for Anomaly Detection: A Survey

arXiv 2507.15905 ↗First posted 21 Jul 2025cs.LG cs.AIcited by 8

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Abstract (arXiv)

In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We explore how these architectures, with their global receptive fields and adaptability, address challenges such as long-range dependency modeling, contextual modeling and data scarcity. The survey categorizes VAD methods into reconstruction-based, feature-based and zero/few-shot approaches, highlighting the paradigm shift brought about by foundation models. By integrating attention mechanisms and leveraging large-scale pre-training, Transformers and foundation models enable more robust, interpretable, and scalable anomaly detection solutions. This work provides a comprehensive review of state-of-the-art techniques, their strengths, limitations, and emerging trends in leveraging these architectures for VAD.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 157 of its 246 references that are in the map. First posted on arXiv 21 Jul 2025.

Builds on

157 of 246 references.

References · 157

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  7. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  8. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  9. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  10. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  11. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  12. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
38 more
  1. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  7. 2014Going Deeper with Convolutionscited by 4,241
  8. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  9. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  10. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  11. 2023Mistral 7Bcited by 3,164
  12. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
  13. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  14. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,711
  15. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  16. 2013Visualizing and Understanding Convolutional Networkscited by 2,290
  17. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  18. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  19. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  20. 2022Visual Prompt Tuningcited by 1,681
  21. 2021ViViT: A Video Vision Transformercited by 1,620
  22. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,299
  23. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
  24. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  25. 2021Transformers in Vision: A Surveycited by 1,013
  26. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  27. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  28. 2020Efficient Transformers: A Surveycited by 790
  29. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  30. 2021Going deeper with Image Transformerscited by 764
  31. 2023CogVLM: Visual Expert for Pretrained Language Modelscited by 708
  32. 2021P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Taskscited by 689
  33. 2022MaPLe: Multi-modal Prompt Learningcited by 687
  34. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 681
  35. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 642
  36. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  37. 2023OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Modelscited by 565
  38. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564

50 most cited of 157 All 157 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 6.

8 papers.

  1. 2025IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detectioncited by 2
  2. 2026RAID: Retrieval-Augmented Anomaly Detectioncited by 2
  3. 2025Adversarially Robust Few-Shot Anomaly Detection with Vision Foundation Modelscited by 0
  4. 2026SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detectioncited by 0
  5. 2026HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detectioncited by 0
  6. 2026Unsupervised Memory-Enhanced Video Transformers: Obstacle Detection for Autonomous Agricultural Rovercited by 0
  7. 2026RareSense: Rarity-Aware Similarity Search for Anomaly Retrieval in Transactional Datacited by 0
  8. 2026Multi-Modal Anomaly Detection: A Surveycited by 0