mlsanityData to 7 Oct 2026

Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild

arXiv 2203.03800 ↗First posted 8 Mar 2022cs.CVcited by 70

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Abstract (arXiv)

Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical yet underexplored. One of the key challenges is that models lack supervision signals from unknown data, producing overconfident predictions on OOD objects. We propose a new unknown-aware object detection framework through Spatial-Temporal Unknown Distillation (STUD), which distills unknown objects from videos in the wild and meaningfully regularizes the model's decision boundary. STUD first identifies the unknown candidate object proposals in the spatial dimension, and then aggregates the candidates across multiple video frames to form a diverse set of unknown objects near the decision boundary. Alongside, we employ an energy-based uncertainty regularization loss, which contrastively shapes the uncertainty space between the in-distribution and distilled unknown objects. STUD establishes the state-of-the-art performance on OOD detection tasks for object detection, reducing the FPR95 score by over 10% compared to the previous best method. Code is available at https://github.com/deeplearning-wisc/stud.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). Builds on 59 of its 73 references that are in the map. First posted on arXiv 8 Mar 2022.

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59 of 73 references.

References · 59

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38 more
  1. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  2. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  3. 2020Designing Network Design Spacescited by 954
  4. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  5. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  6. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  7. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  8. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  9. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  10. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  11. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
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  14. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  15. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  16. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  17. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  18. 2021Towards Open World Object Detectioncited by 297
  19. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  20. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  21. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 220
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  25. 2020Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encodercited by 145
  26. 2017Dropout Sampling for Robust Object Detection in Open-Set Conditionscited by 112
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  29. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
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  31. 2020Incremental Few-Shot Object Detectioncited by 102
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 10; 2023: 18; 2024: 24; 2025: 12; 2026: 6.

70 papers.

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  1. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  2. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 267
  3. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 251
  4. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  5. 2023Non-Parametric Outlier Synthesiscited by 129
  6. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
  7. 2023LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learningcited by 107
  8. 2024ARGS: Alignment as Reward-Guided Searchcited by 99
  9. 2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
  10. 2024Process Reward Model with Q-Value Rankingscited by 78
  11. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  12. 2024Negative Label Guided OOD Detection with Pretrained Vision-Language Modelscited by 66
38 more
  1. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  2. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  3. 2022SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detectioncited by 33
  4. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  5. 2023Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objectscited by 23
  6. 2024LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Modelscited by 21
  7. 2025Secure On-Device Video OOD Detection Without Backpropagationcited by 19
  8. 2023Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detectioncited by 16
  9. 2024AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Modelscited by 16
  10. 2023On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingcited by 15
  11. 2023How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?cited by 15
  12. 2024AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Drivingcited by 14
  13. 2024Open World Object Detection: A Surveycited by 13
  14. 2022Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samplescited by 12
  15. 2024RoDLA: Benchmarking the Robustness of Document Layout Analysis Modelscited by 12
  16. 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognitioncited by 10
  17. 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
  18. 2023Model-free Test Time Adaptation for Out-Of-Distribution Detectioncited by 9
  19. 2024On the Learnability of Out-of-distribution Detectioncited by 8
  20. 2022Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networkscited by 7
  21. 2023Exploring the Potential of World Models for Anomaly Detection in Autonomous Drivingcited by 7
  22. 2023Out-of-Distribution Detection for Monocular Depth Estimationcited by 7
  23. 2023Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learningcited by 7
  24. 2023Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contextscited by 6
  25. 2023Low-Dimensional Gradient Helps Out-of-Distribution Detectioncited by 6
  26. 2024Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representationscited by 6
  27. 2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?cited by 6
  28. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  29. 2023Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?cited by 4
  30. 2024Optimal Parameter and Neuron Pruning for Out-of-Distribution Detectioncited by 4
  31. 2024Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesiscited by 4
  32. 2025Out-of-Distribution Detection using Synthetic Data Generationcited by 4
  33. 2025RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representationscited by 4
  34. 2025Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detectioncited by 4
  35. 2024Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directionscited by 2
  36. 2024FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detectioncited by 2
  37. 2025Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statisticscited by 2
  38. 2026Detecting Unknown Objects via Energy-based Separation for Open World Object Detectioncited by 2

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