mlsanityData to 7 Oct 2026

A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation

arXiv 2308.13331 ↗First posted 25 Aug 2023cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

The task of semantic segmentation requires a model to assign semantic labels to each pixel of an image. However, the performance of such models degrades when deployed in an unseen domain with different data distributions compared to the training domain. We present a new augmentation-driven approach to domain generalization for semantic segmentation using a re-parameterized vision transformer (ReVT) with weight averaging of multiple models after training. We evaluate our approach on several benchmark datasets and achieve state-of-the-art mIoU performance of 47.3% (prior art: 46.3%) for small models and of 50.1% (prior art: 47.8%) for midsized models on commonly used benchmark datasets. At the same time, our method requires fewer parameters and reaches a higher frame rate than the best prior art. It is also easy to implement and, unlike network ensembles, does not add any computational complexity during inference.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 35 of its 51 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

35 of 51 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  8. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  9. 2022Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timecited by 1,366
  10. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,194
  11. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  12. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
23 more
  1. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  2. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  3. 2013Domain Generalization via Invariant Feature Representationcited by 622
  4. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  5. 2018Two at Once: Enhancing Learning and Generalization Capacities via IBN-Netcited by 361
  6. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  7. 2020Learning to Learn Single Domain Generalizationcited by 295
  8. 2019Episodic Training for Domain Generalizationcited by 245
  9. 2019Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Datacited by 245
  10. 2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
  11. 2020ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learningcited by 209
  12. 2021FSDR: Frequency Space Domain Randomization for Domain Generalizationcited by 163
  13. 2020Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentationcited by 153
  14. 2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
  15. 2021PixMix: Dreamlike Pictures Comprehensively Improve Safety Measurescited by 131
  16. 2017Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenescited by 122
  17. 2022Semantic-Aware Domain Generalized Segmentationcited by 78
  18. 2022WildNet: Learning Domain Generalized Semantic Segmentation from the Wildcited by 75
  19. 2018Efficient Decentralized Deep Learning by Dynamic Model Averagingcited by 30
  20. 2023Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Drivingcited by 29
  21. 2020Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representationscited by 24
  22. 2022Continual BatchNorm Adaptation (CBNA) for Semantic Segmentationcited by 12
  23. 2022Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 2; 2026: 1.

8 papers.

  1. 2023Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentationcited by 82
  2. 2023Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentationcited by 27
  3. 2023Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learningcited by 20
  4. 2024Diffusion Features to Bridge Domain Gap for Semantic Segmentationcited by 5
  5. 2025Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalizationcited by 2
  6. 2024A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Modelscited by 0
  7. 2025Domain Generalization for Semantic Segmentation: A Surveycited by 0
  8. 2026Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentationcited by 0