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Deep Deterministic Uncertainty for Semantic Segmentation

arXiv 2111.00079 ↗First posted 29 Oct 2021cs.CV cs.LGcited by 24

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Abstract (arXiv)

We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. DDU enables quantifying and disentangling epistemic and aleatoric uncertainty in a single forward pass through the model. We study the similarity of feature representations of pixels at different locations for the same class and conclude that it is feasible to apply DDU location independently, which leads to a significant reduction in memory consumption compared to pixel dependent DDU. Using the DeepLab-v3+ architecture on Pascal VOC 2012, we show that DDU improves upon MC Dropout and Deep Ensembles while being significantly faster to compute.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 29 Oct 2021.

Builds on

13 of 22 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  5. 2016Pyramid Scene Parsing Networkcited by 2,479
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  8. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  9. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  10. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  11. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  12. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
1 more
  1. 2020Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologiescited by 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 5; 2024: 11; 2025: 2; 2026: 1.

24 papers.

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  1. 2024NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wildcited by 85
  2. 2023Calibration in Deep Learning: A Survey of the State-of-the-Artcited by 75
  3. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  4. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  5. 2024ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentationcited by 25
  6. 2022ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inferencecited by 15
  7. 2023A Data-Driven Measure of Relative Uncertainty for Misclassification Detectioncited by 14
  8. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  9. 2024Enabling Uncertainty Estimation in Iterative Neural Networkscited by 11
  10. 2025Uncertainty Estimation by Flexible Evidential Deep Learningcited by 10
  11. 2024Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecastingcited by 6
  12. 2024Bayesian Active Learning for Semantic Segmentationcited by 5
12 more
  1. 2024Uncertainty Estimation for 3D Object Detection via Evidential Learningcited by 5
  2. 2022Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detectioncited by 4
  3. 2022Uncertainty in Real-Time Semantic Segmentation on Embedded Systemscited by 3
  4. 2024Risk-aware Classification via Uncertainty Quantificationcited by 3
  5. 2022Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHScited by 2
  6. 2023ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentationcited by 2
  7. 2024Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Functioncited by 2
  8. 2025Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theorycited by 2
  9. 2023Uncertainty estimation in Deep Learning for Panoptic segmentationcited by 1
  10. 2024Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentationcited by 1
  11. 2024Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantificationcited by 0
  12. 2026SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentationcited by 0