HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

• URL: http://arxiv.org/abs/2204.13132v1
• Date: Wed, 27 Apr 2022 18:00:26 GMT
• Title: HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
• Authors: Lukas Hoyer, Dengxin Dai, Luc Van Gool
• Abstract summary: Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain to the target domain. We propose HRDA, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to preserve fine segmentation details. It significantly improves the state-of-the-art performance by 5.5 mIoU for GTA-to-Cityscapes and 4.9 mIoU for Synthia-to-Cityscapes.
• Score: 104.47737619026246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further annotations on the target domain. This work focuses on UDA for semantic segmentation as real-world pixel-wise annotations are particularly expensive to acquire. 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-to-Cityscapes and 4.9 mIoU for Synthia-to-Cityscapes, resulting in unprecedented 73.8 and 65.8 mIoU, respectively. The implementation is available at https://github.com/lhoyer/HRDA.

Related papers

• MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation [155.0797148367653]
  Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain and an unlabeled target domain. We propose to leverage geometric information, i.e., depth predictions, as depth discontinuities often coincide with segmentation boundaries. We show that our method can be plugged into various recent UDA methods and consistently improve results across standard UDA benchmarks.
  arXiv  Detail & Related papers  (2024-08-29T12:15:10Z)
• Style Adaptation for Domain-adaptive Semantic Segmentation [2.1365683052370046]
  Domain discrepancy leads to a significant decrease in the performance of general network models trained on the source domain data when applied to the target domain. We introduce a straightforward approach to mitigate the domain discrepancy, which necessitates no additional parameter calculations and seamlessly integrates with self-training-based UDA methods. Our proposed method attains a noteworthy UDA performance of 76.93 mIoU on the GTA->Cityscapes dataset, representing a notable improvement of +1.03 percentage points over the previous state-of-the-art results.
  arXiv  Detail & Related papers  (2024-04-25T02:51:55Z)
• Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image Segmentation [108.33885637197614]
  Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine learning models trained on a source domain to perform well on unlabeled or unseen target domains. We propose HRDA, a multi-resolution framework for UDA&DG, 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.
  arXiv  Detail & Related papers  (2023-04-26T15:18:45Z)
• I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation [55.633859439375044]
  Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. Key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
  arXiv  Detail & Related papers  (2023-01-03T15:19:48Z)
• Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation [25.626882426111198]
  Unsupervised domain adaptation (UDA) for semantic segmentation has recently gained increasing research attention. In this paper, we propose a novel two-stage entropy-based UDA method for semantic segmentation. Our method achieves state-of-the-art 58.4% and 59.6% mIoUs on SYNTHIA-to-Cityscapes and GTA5-to-Cityscapes using DeepLabV2 and competitive performance using the lightweight BiSeNet.
  arXiv  Detail & Related papers  (2022-08-23T03:48:48Z)
• Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation [51.15061013818216]
  Supervised approaches have led to great success with the advance of deep learning, but they rely on large quantities of ground-truth depth annotations. Unsupervised domain adaptation (UDA) transfers knowledge from labeled source data to unlabeled target data, so as to relax the constraint of supervised learning. We propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components.
  arXiv  Detail & Related papers  (2022-07-30T08:05:35Z)
• Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation [19.859764556851434]
  We propose to exploit self-supervised monocular depth estimation to improve UDA for semantic segmentation. Our whole proposal allows for achieving state-of-the-art performance (58.8 mIoU) in the GTA5->CS benchmark benchmark.
  arXiv  Detail & Related papers  (2021-10-13T12:48:51Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.