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[ECCV22] Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
| Date | Stars |
|---|---|
| 2026-07-24 | 267 |
| 2026-07-25 | 267 |
| 2026-07-28 | 267 |
| 2026-07-30 | 267 |
| 2026-08-06 | 267 |
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## HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
**by [Lukas Hoyer](https://lhoyer.github.io/), [Dengxin Dai](https://vas.mpi-inf.mpg.de/dengxin/), and [Luc Van Gool](https://scholar.google.de/citations?user=TwMib_QAAAAJ&hl=en)**
**[[ECCV22 Paper]](https://arxiv.org/pdf/2204.13132)**
**[[Extension Paper]](https://arxiv.org/pdf/2304.13615.pdf)**
:bell: **News:**
* [2024-07-03] We are happy to announce that our work [SemiVL](https://github.com/google-research/semivl) on semi-supervised semantic segmentation with vision-language guidance was accepted at **ECCV24**.
* [2024-07-03] We are happy to announce that our follow-up work [DGInStyle](https://dginstyle.github.io/) on image diffusion for domain-generalizable semantic segmentation was accepted at **ECCV24**.
* [2023-09-26] We are happy to announce that our [Extension Paper](https://arxiv.org/pdf/2304.13615.pdf) on domain generalization and clear-to-adverse-weather UDA was accapted at **PAMI**.
* [2023-08-25] We are happy to announce that our follow-up work [EDAPS](https://github.com/susaha/edaps) on panoptic segmentation UDA was accepted at **ICCV23**.
* [2023-04-27] We further extend HRDA to domain generalization and clear-to-adverse-weather UDA in the [Extension Paper](https://arxiv.org/pdf/2304.13615.pdf).
* [2023-02-28] We are happy to announce that our follow-up work [MIC](https://github.com/lhoyer/MIC) on context-enhanced UDA was accepted at **CVPR23**.
* [2022-07-05] We are happy to announce that HRDA was accepted at **ECCV22**.
## Overview
**Unsupervised domain adaptation (UDA)** aims to adapt a model trained on
synthetic data to real-world data without requiring expensive annotations of
real-world images. As UDA methods for semantic segmentation are usually GPU
memory intensive, most **previous methods operate only on downscaled images**.
We question this design as low-resolution predictions often fail to preserve
fine details. The alternative of training with random crops of high-resolution
images alleviates this problem but falls short in capturing long-range,
domain-robust context information.
Therefore, we propose **HRDA**, a multi-resolution training approach for UDA,
that combines the strengths of small high-resolution crops to **preserve fine
segmentation details** and large low-resolution crops to **capture long-range
context dependencies** with a learned scale attention, while maintaining a
**manageable GPU memory** footprint.
HRDA enables adapting small objects and preserving fine segmentation details.
It significantly improves the state-of-the-art performance **by 5.5 mIoU for
GTA→Cityscapes** and **by 4.9 mIoU for Synthia→Cityscapes**, resulting in an
unprecedented performance of 73.8 and 65.8 mIoU, respectively.
The more detailed domain-adaptive semantic segmentation of HRDA, compared to
the previous state-of-the-art UDA method DAFormer, can also be observed in
example predictions from the Cityscapes validation set.
https://user-images.githubusercontent.com/1277888/181128057-27b8039f-a4c9-4f6d-9aa8-9b7f364d8921.mp4
HRDA can be further **extended to domain generalization** lifting the requirement
of access to target images. Also in domain generalization,
HRDA significantly improves the state-of-the-art performance by **+4.2 mIoU**.
For more information on HRDA, please check our
[[ECCV Paper]](https://arxiv.org/pdf/2204.13132) and the [[Extension Paper]](https://arxiv.org/pdf/2304.13615.pdf).
If you find HRDA useful in your research, please consider citing:
```
@InProceedings{hoyer2022hrda,
title={{HRDA}: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation},
author={Hoyer, Lukas and Dai, Dengxin and Van Gool, Luc},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={372--391},
year={20Excerpt of 18,925 characters
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