mlsanityData to 14 Oct 2026

The Robust Semantic Segmentation UNCV2023 Challenge Results

arXiv 2309.15478 ↗First posted 27 Sep 2023cs.CV cs.LGcited by 5

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Abstract (arXiv)

This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies presented at prominent conferences in the fields of computer vision and machine learning and journals over the past few years. Within this document, the challenge is introduced, shedding light on its purpose and objectives, which primarily revolved around enhancing the robustness of semantic segmentation in urban scenes under varying natural adversarial conditions. The report then delves into the top-performing solutions. Moreover, the document aims to provide a comprehensive overview of the diverse solutions deployed by all participants. By doing so, it seeks to offer readers a deeper insight into the array of strategies that can be leveraged to effectively handle the inherent uncertainties associated with autonomous driving and semantic segmentation, especially within urban environments.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 43 of its 53 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

43 of 53 references.

References · 43

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2017On Calibration of Modern Neural Networkscited by 4,960
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  8. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  9. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  10. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  11. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  12. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
31 more
  1. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  2. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  3. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,271
  4. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
  5. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  6. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 482
  7. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  8. 2020BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learningcited by 371
  9. 2020Calibrating Deep Neural Networks using Focal Losscited by 357
  10. 2020Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shiftcited by 236
  11. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  12. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 178
  13. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  14. 2020Training independent subnetworks for robust predictioncited by 177
  15. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 164
  16. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  17. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  18. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  19. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  20. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  21. 2020Masksembles for Uncertainty Estimationcited by 88
  22. 2021Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenescited by 83
  23. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  24. 2022Packed-Ensembles for Efficient Uncertainty Estimationcited by 49
  25. 2023Unmasking Anomalies in Road-Scene Segmentationcited by 45
  26. 2021Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentationcited by 40
  27. 2019TRADI: Tracking deep neural network weight distributions for uncertainty estimationcited by 39
  28. 2022MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and taskscited by 24
  29. 2023Long Horizon Temperature Scalingcited by 18
  30. 2022Latent Discriminant deterministic Uncertaintycited by 17
  31. 2020Post-hoc Calibration of Neural Networks by g-Layerscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shiftscited by 5
  2. 2025Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustnesscited by 3
  3. 2026A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helpscited by 2
  4. 2024Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearancescited by 1
  5. 2024The Explanation Necessity for Healthcare AIcited by 0