# Reliability in Semantic Segmentation: Are We on the Right Track?

arXiv: https://arxiv.org/abs/2303.11298 · HTML: https://mlsanity.com/paper/2303.11298/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 62 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 30 of 53)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)

## Built on by (most cited first, 16 of 16)

- [Reliability in Semantic Segmentation: Can We Use Synthetic Data?](https://mlsanity.com/paper/2312.09231/) (2023)
- [Placing Objects in Context via Inpainting for Out-of-distribution Segmentation](https://mlsanity.com/paper/2402.16392/) (2024)
- [Benchmarking Segmentation Models with Mask-Preserved Attribute Editing](https://mlsanity.com/paper/2403.01231/) (2024)
- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning](https://mlsanity.com/paper/2409.18026/) (2024)
- [What could go wrong? Discovering and describing failure modes in computer vision](https://mlsanity.com/paper/2408.04471/) (2024)
- [Where are we with calibration under dataset shift in image classification?](https://mlsanity.com/paper/2507.07780/) (2025)
- [AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images](https://mlsanity.com/paper/2312.06106/) (2023)
- [Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness](https://mlsanity.com/paper/2506.05917/) (2025)
- [Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling](https://mlsanity.com/paper/2401.09245/) (2024)
- [Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence](https://mlsanity.com/paper/2405.06265/) (2024)
- [Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing](https://mlsanity.com/paper/2603.01535/) (2026)
- [Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning](https://mlsanity.com/paper/2305.11347/) (2023)
- [Transient Fault Tolerant Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2408.16952/) (2024)
- [DA-Cal: Towards Cross-Domain Calibration in Semantic Segmentation](https://mlsanity.com/paper/2602.20860/) (2026)
- [PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation](https://mlsanity.com/paper/2307.07489/) (2023)

## Abstract (arXiv)

Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance follows a constant, upward trend, properties like robustness or uncertainty estimation are less explored -leaving doubts about advances in model reliability. Studies along these axes exist, but they are mainly limited to classification models. In contrast, we carry out a study on semantic segmentation, a relevant task for many real-world applications where model reliability is paramount. We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection. We find that while recent models are significantly more robust, they are not overall more reliable in terms of uncertainty estimation. We further explore methods that can come to the rescue and show that improving calibration can also help with other uncertainty metrics such as misclassification or OOD detection. This is the first study on modern segmentation models focused on both robustness and uncertainty estimation and we hope it will help practitioners and researchers interested in this fundamental vision task. Code available at https://github.com/naver/relis.