mlsanityData to 14 Oct 2026

Scaling Out-of-Distribution Detection for Real-World Settings

arXiv 1911.11132 ↗First posted 25 Nov 2019cs.CV cs.LGcited by 465

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Cited by 20 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, existing research mainly focuses on simple small-scale settings. To set the stage for more realistic out-of-distribution detection, we depart from small-scale settings and explore large-scale multiclass and multi-label settings with high-resolution images and thousands of classes. To make future work in real-world settings possible, we create new benchmarks for three large-scale settings. To test ImageNet multiclass anomaly detectors, we introduce the Species dataset containing over 700,000 images and over a thousand anomalous species. We leverage ImageNet-21K to evaluate PASCAL VOC and COCO multilabel anomaly detectors. Third, we introduce a new benchmark for anomaly segmentation by introducing a segmentation benchmark with road anomalies. We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.

Key facts. Cited by 465 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 45; 2023: 112; 2024: 113; 2025: 115; 2026: 62.

465 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 694
  3. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  4. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  5. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  6. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 197
  7. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 179
  8. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 160
  9. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  10. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  11. 2022Leveraging Unlabeled Data to Predict Out-of-Distribution Performancecited by 145
  12. 2023In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluationcited by 134
38 more
  1. 2023CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say Nocited by 130
  2. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  3. 2023LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learningcited by 107
  4. 2022Plex: Towards Reliability using Pretrained Large Model Extensionscited by 102
  5. 2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
  6. 2021Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenescited by 83
  7. 2021Types of Out-of-Distribution Texts and How to Detect Themcited by 80
  8. 2021Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentationcited by 77
  9. 2023Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancementcited by 77
  10. 2023Self-Evaluation Improves Selective Generation in Large Language Modelscited by 76
  11. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  12. 2021Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environmentscited by 66
  13. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  14. 2023Nearest Neighbor Guidance for Out-of-Distribution Detectioncited by 64
  15. 2022A Close Look into the Calibration of Pre-trained Language Modelscited by 57
  16. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 57
  17. 2022Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentationcited by 55
  18. 2024Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinationscited by 54
  19. 2024Driving Everywhere with Large Language Model Policy Adaptationcited by 53
  20. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  21. 2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policiescited by 52
  22. 2021Deep Metric Learning for Open World Semantic Segmentationcited by 51
  23. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 49
  24. 2022Improving Zero-shot Generalization and Robustness of Multi-modal Modelscited by 45
  25. 2022Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interferencecited by 44
  26. 2024A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)cited by 44
  27. 2023Out-of-distribution Detection with Implicit Outlier Transformationcited by 43
  28. 2024Learning Transferable Negative Prompts for Out-of-Distribution Detectioncited by 43
  29. 2022DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognitioncited by 42
  30. 2023On Advantages of Mask-level Recognition for Outlier-aware Segmentationcited by 42
  31. 2023NECO: NEural Collapse Based Out-of-distribution detectioncited by 42
  32. 2021How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Reviewcited by 39
  33. 2023A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Modelscited by 39
  34. 2023Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimizationcited by 39
  35. 2023OpenMix: Exploring Outlier Samples for Misclassification Detectioncited by 38
  36. 2023How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?cited by 38
  37. 2023Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolationcited by 38
  38. 2023Learning to Augment Distributions for Out-of-Distribution Detectioncited by 38

50 most cited of 465 All 465 (JSON)