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BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation

arXiv 2308.16819 ↗First posted 31 Aug 2023cs.CVcited by 0

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Abstract (arXiv)

We introduce BTSeg (Barlow Twins regularized Segmentation), an innovative, semi-supervised training approach enhancing semantic segmentation models in order to effectively tackle adverse weather conditions without requiring additional labeled training data. Images captured at similar locations but under varying adverse conditions are regarded as manifold representation of the same scene, thereby enabling the model to conceptualize its understanding of the environment. BTSeg shows cutting-edge performance for the new challenging ACG benchmark and sets a new state-of-the-art for weakly-supervised domain adaptation for the ACDC dataset. To support further research, we have made our code publicly available at https://github.com/fraunhoferhhi/BTSeg .

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 31 Aug 2023.

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References · 26

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  3. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
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  5. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  6. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
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  12. 2017Semantic Foggy Scene Understanding with Synthetic Datacited by 403
14 more
  1. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 300
  2. 2020Hierarchical Multi-Scale Attention for Semantic Segmentationcited by 183
  3. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 164
  4. 2018Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understandingcited by 98
  5. 2020Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentationcited by 86
  6. 2022An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditionscited by 54
  7. 2019Physics-Based Rendering for Improving Robustness to Raincited by 49
  8. 2019Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understandingcited by 40
  9. 2022Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditionscited by 31
  10. 2021A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learningcited by 26
  11. 2021Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIscited by 15
  12. 2021BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net modelscited by 12
  13. 2023Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentationcited by 12
  14. 2022Semantic Self-adaptation: Enhancing Generalization with a Single Samplecited by 8

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