mlsanityData to 7 Oct 2026

UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving

arXiv 2406.06370 ↗First posted 10 Jun 2024cs.CV cs.ROcited by 3

On the map: End-to-end autonomous driving → VLMs for autonomous driving

Abstract (arXiv)

Dealing with atypical traffic scenarios remains a challenging task in autonomous driving. However, most anomaly detection approaches cannot be trained on raw sensor data but require exposure to outlier data and powerful semantic segmentation models trained in a supervised fashion. This limits the representation of normality to labeled data, which does not scale well. In this work, we revisit unsupervised anomaly detection and present UMAD, leveraging generative world models and unsupervised image segmentation. Our method outperforms state-of-the-art unsupervised anomaly detection.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving. Builds on 37 of its 54 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

37 of 54 references.

References · 37

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  8. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  9. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 337
  10. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  11. 2017Concrete Dropoutcited by 227
  12. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
25 more
  1. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  2. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  3. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  4. 2019The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentationcited by 122
  5. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  6. 2023Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusioncited by 105
  7. 2022Anomaly Detection in Autonomous Driving: A Surveycited by 87
  8. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  9. 2023Segment Any Anomaly without Training via Hybrid Prompt Regularizationcited by 59
  10. 2022Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentationcited by 55
  11. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  12. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  13. 2022DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognitioncited by 42
  14. 2023Maskomaly:Zero-Shot Mask Anomaly Segmentationcited by 34
  15. 2023Unsupervised Universal Image Segmentationcited by 34
  16. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  17. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  18. 2023MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representationscited by 33
  19. 2022MAEDAY: MAE for few and zero shot AnomalY-Detectioncited by 30
  20. 2023Segment Every Out-of-Distribution Objectcited by 21
  21. 2019Anomaly Detection with Adversarial Dual Autoencoderscited by 16
  22. 2023Perception Datasets for Anomaly Detection in Autonomous Driving: A Surveycited by 16
  23. 2023UGainS: Uncertainty Guided Anomaly Instance Segmentationcited by 15
  24. 2024AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Drivingcited by 14
  25. 2023Exploring the Potential of World Models for Anomaly Detection in Autonomous Drivingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Survey of World Models for Autonomous Drivingcited by 60
  2. 2025Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Modelscited by 60
  3. 2026NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessmentcited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2026One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
  3. 2025Unified Unsupervised Anomaly Detection via Matching Cost Filtering
  4. 2026Vision-Language Feature Alignment for Road Anomaly Segmentation
  5. 2025Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
  6. 2026Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models
  7. 2025Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation
  8. 2024VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection