# Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Source: [Effective Use of Synthetic Data for Urban Scene Semantic Segmentation](<https://www.sota2.com/research/paper/68fa649e1e209131cea05ad7>)

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

Data updated: 2025-10-25T21:08:02.971Z

Authors: Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M. Alvarez

Published: 2018-07-16T22:10:09.000Z

## Abstract

Training a deep network to perform semantic segmentation requires large amounts of labeled data. To alleviate the manual effort of annotating real images, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images during training. In this paper, we introduce a drastically different way to handle synthetic images that does not require seeing any real images at training time. Our approach builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently. In particular, the former should be handled in a detection-based manner to better account for the fact that, while their texture in synthetic images is not photo-realistic, their shape looks natural. Our experiments evidence the effectiveness of our approach on Cityscapes and CamVid with models trained on synthetic data only.

## 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.

- [Semantic Segmentation on Cityscapes GTA5 to Cityscapes adaptation (val)](<https://www.sota2.com/research/sota/semantic-segmentation-on-cityscapes-gta5-to-cityscapes-adaptation-val.md>): Semantic segmentation; Cityscapes GTA5 to Cityscapes adaptation (val); mIoU (Overall): 42.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.06132>)

[PDF](<https://arxiv.org/pdf/1807.06132.pdf>)