Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

• URL: http://arxiv.org/abs/2110.02833v1
• Date: Wed, 6 Oct 2021 15:05:48 GMT
• Title: Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries
• Authors: Adriano Cardace, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
• Abstract summary: Deep neural networks fail to generalize towards new domains when performing synthetic-to-real adaptation. In this work, we present a novel low-level adaptation strategy that allows us to obtain sharp predictions. We also introduce an effective data augmentation that alleviates the noise typically present at semantic boundaries when employing pseudo-labels for self-training.
• Score: 21.6953660626021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation. Such domain shift is particularly noticeable along class boundaries, invalidating one of the main goals of semantic segmentation that consists in obtaining sharp segmentation masks. In this work, we specifically address this core problem in the context of Unsupervised Domain Adaptation and present a novel low-level adaptation strategy that allows us to obtain sharp predictions. Moreover, inspired by recent self-training techniques, we introduce an effective data augmentation that alleviates the noise typically present at semantic boundaries when employing pseudo-labels for self-training. Our contributions can be easily integrated into other popular adaptation frameworks, and extensive experiments show that they effectively improve performance along class boundaries.

Related papers

• Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection [81.703478548177]
  Training models dedicated to semantic segmentation require a large amount of pixel-wise annotated data. Unsupervised domain adaptation approaches aim at aligning the feature distributions between the labeled source and the unlabeled target data. Previous works attempted to include human interactions in this process under the form of sparse single-pixel annotations in the target data. We propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
  arXiv  Detail & Related papers  (2022-06-01T01:52:28Z)
• 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)
• Adapting Segmentation Networks to New Domains by Disentangling Latent Representations [14.050836886292869]
  Domain adaptation approaches have come into play to transfer knowledge acquired on a label-abundant source domain to a related label-scarce target domain. We propose a novel performance metric to capture the relative efficacy of an adaptation strategy compared to supervised training.
  arXiv  Detail & Related papers  (2021-08-06T09:43:07Z)
• Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning [62.7588467386166]
  We leverage contrastive learning to bridge the domain gap by aligning the features of structurally similar label patches across domains. Our approach consistently outperforms state-of-the-art unsupervised and semi-supervised methods on two challenging domain adaptive segmentation tasks.
  arXiv  Detail & Related papers  (2021-04-22T13:39:12Z)
• Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation [14.050836886292869]
  We introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. We verify the effectiveness of such methods in the autonomous driving setting.
  arXiv  Detail & Related papers  (2021-04-06T16:07:22Z)
• Gradient Regularized Contrastive Learning for Continual Domain Adaptation [86.02012896014095]
  We study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. We propose Gradient Regularized Contrastive Learning (GRCL) to solve the obstacles. Experiments on Digits, DomainNet and Office-Caltech benchmarks demonstrate the strong performance of our approach.
  arXiv  Detail & Related papers  (2021-03-23T04:10:42Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings [25.137859989323537]
  We propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method. We introduce two novel learning objectives to enhance the discriminative clustering performance.
  arXiv  Detail & Related papers  (2020-11-25T10:06:22Z)
• Domain Adversarial Fine-Tuning as an Effective Regularizer [80.14528207465412]
  In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. Standard fine-tuning can degrade the general-domain representations captured during pretraining. We introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer.
  arXiv  Detail & Related papers  (2020-09-28T14:35:06Z)
• Learning to adapt class-specific features across domains for semantic segmentation [36.36210909649728]
  In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. We adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network.
  arXiv  Detail & Related papers  (2020-01-22T23:51:30Z)

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.