# BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation

Source: [BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation](<https://www.sota2.com/research/paper/68fa74cc1e209131cea63e6e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:18:28.201Z

Authors: Johannes K\\"unzel, Anna Hilsmann, Peter Eisert

Published: 2023-08-31T15:49:53.000Z

## Abstract

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 .

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.16819>)

[PDF](<https://arxiv.org/pdf/2308.16819.pdf>)