mlsanityData to 14 Oct 2026

Dropout Sampling for Robust Object Detection in Open-Set Conditions

arXiv 1710.06677 ↗First posted 18 Oct 2017cs.CVcited by 112

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Abstract (arXiv)

Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We evaluate this approach on a large synthetic dataset of 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. A Dropout Sampling network is shown to achieve a 12.3% increase in recall (for the same precision score as a standard network) and a 15.1% increase in precision (for the same recall score as the standard network).

Key facts. Cited by 112 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2017.

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Citing papers per year: 2021: 20; 2022: 23; 2023: 26; 2024: 21; 2025: 10; 2026: 12.

112 papers.

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  1. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  2. 2021Towards Open World Object Detectioncited by 297
  3. 2021OW-DETR: Open-world Detection Transformercited by 129
  4. 2024VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimationcited by 73
  5. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  6. 2023A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Futurecited by 61
  7. 2022PROB: Probabilistic Objectness for Open World Object Detectioncited by 57
  8. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  9. 2021Opening up Open-World Trackingcited by 50
  10. 2024Better Call SAL: Towards Learning to Segment Anything in Lidarcited by 47
  11. 2022Uncertainty Quantification of Collaborative Detection for Self-Drivingcited by 46
  12. 2022Towards Open-Set Object Detection and Discoverycited by 44
38 more
  1. 2022UC-OWOD: Unknown-Classified Open World Object Detectioncited by 40
  2. 2021Exemplar-Based Open-Set Panoptic Segmentation Networkcited by 38
  3. 2022Expanding Low-Density Latent Regions for Open-Set Object Detectioncited by 38
  4. 2022SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detectioncited by 33
  5. 2021A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detectioncited by 31
  6. 2022Revisiting Open World Object Detectioncited by 30
  7. 2021Open-set 3D Object Detectioncited by 29
  8. 2023CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detectioncited by 28
  9. 2023Collaborative Multi-Object Tracking with Conformal Uncertainty Propagationcited by 27
  10. 2021Uncertainty for Identifying Open-Set Errors in Visual Object Detectioncited by 26
  11. 2023Random Boxes Are Open-world Object Detectorscited by 24
  12. 2022OpenTAL: Towards Open Set Temporal Action Localizationcited by 23
  13. 2023Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objectscited by 23
  14. 2023Towards Unsupervised Object Detection From LiDAR Point Cloudscited by 23
  15. 2021Monte Carlo DropBlock for Modelling Uncertainty in Object Detectioncited by 20
  16. 2021Objects in Semantic Topologycited by 18
  17. 2023Navigating Open Set Scenarios for Skeleton-based Action Recognitioncited by 17
  18. 2021Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectorscited by 16
  19. 2023Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detectioncited by 16
  20. 2023Identifying Label Errors in Object Detection Datasets by Loss Inspectioncited by 16
  21. 2023Bayesian Deep Learning for Affordance Segmentation in imagescited by 13
  22. 2024Open-Set Recognition in the Age of Vision-Language Modelscited by 13
  23. 2024Open World Object Detection: A Surveycited by 13
  24. 2022Learning to Discover and Detect Objectscited by 11
  25. 2023EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learningcited by 10
  26. 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
  27. 2022Segmenting Known Objects and Unseen Unknowns without Prior Knowledgecited by 9
  28. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 9
  29. 2023Activate and Reject: Towards Safe Domain Generalization under Category Shiftcited by 9
  30. 2021Bayesian Active Learning for Sim-to-Real Robotic Perceptioncited by 8
  31. 2022Towards Generalized Few-Shot Open-Set Object Detectioncited by 8
  32. 2023SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Drivingcited by 8
  33. 2021Active learning with MaskAL reduces annotation effort for training Mask R-CNNcited by 7
  34. 2022What's in the Black Box? The False Negative Mechanisms Inside Object Detectorscited by 7
  35. 2022Large-Scale Open-Set Classification Protocols for ImageNetcited by 7
  36. 2022Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learningcited by 7
  37. 2023Probabilistic Circuits That Know What They Don't Knowcited by 7
  38. 20233D Indoor Instance Segmentation in an Open-Worldcited by 7

50 most cited of 112 All 112 (JSON)