# Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation

Source: [Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation](<https://www.sota2.com/research/paper/68fa66b81e209131cea1213e>)

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

Data updated: 2025-10-25T21:57:21.263Z

Authors: Oluwafemi Azeez

Published: 2019-11-20T07:36:45.000Z

## Abstract

It is expensive to generate real-life image labels and there is a domain gap between real-life and simulated images, hence a model trained on the latter cannot adapt to the former. Solving this can totally eliminate the need for labeling real-life datasets completely. Class balanced self-training is one of the existing techniques that attempt to reduce the domain gap. Moreover, augmenting RGB with flow maps has improved performance in simple semantic segmentation and geometry is preserved across domains. Hence, by augmenting images with dense optical flow map, domain adaptation in semantic segmentation can be improved.

## 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/1911.09652>)

[PDF](<https://arxiv.org/pdf/1911.09652.pdf>)