Domain Adaptation

Unsupervised Domain Adapatation (UDA) for semantic segmentation is the task of aligning a network trained on source data to perform well on target data with a related but different statistical distribution. Complex deep neural networks for this task require to be trained with a huge amount of labeled data, which is difficult and expensive to acquire. A recently proposed workaround is to use synthetic data, however the differences between real world and synthetic scenes limit the performance. UDA techniques allow to reduce this gap allowing to obtain reliable performances on the target domain. We presented an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.

Key research topics include:

  • We proposed an unsupervised domain adaptation strategy that combines adversarial learning and self-teaching.
  • An improved adversarial module driven by a couple of fully convolutional discriminators dealing with different domains is introduced.
  • We propose an UDA strategy to address the domain shift issue between real world and synthetic representations focusing on mobile devices.
  • We propose a novel Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution
  • The combination of UDA and Continual Learning has been tackled.
  • Domain adaptation has also been tackled in a federated learning setting.

Recent publications:

Shenaj, Donald; Barbato, Francesco; Michieli, Umberto; Zanuttigh, Pietro

Continual coarse-to-fine domain adaptation in semantic segmentation Journal Article

In: Image and Vision Computing, vol. 121, pp. 104426, 2022.

Links | BibTeX

Toldo, Marco; Michieli, Umberto; Zanuttigh, Pietro

Learning with Style: Continual Semantic Segmentation Across Tasks and Domains Journal Article

In: arXiv preprint arXiv:2210.07016, 2022.

BibTeX

Toldo, Marco; Michieli, Umberto; Zanuttigh, Pietro

Unsupervised domain adaptation in semantic segmentation via orthogonal and clustered embeddings Proceedings Article

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1358–1368, 2021.

BibTeX

Barbato, Francesco; Michieli, Umberto; Toldo, Marco; Zanuttigh, Pietro

Adapting Segmentation Networks to New Domains by Disentangling Latent Representations. Journal Article

In: CoRR, 2021.

BibTeX

Michieli, Umberto; Biasetton, Matteo; Agresti, Gianluca; Zanuttigh, Pietro

Adversarial learning and self-teaching techniques for domain adaptation in semantic segmentation Journal Article

In: IEEE Transactions on Intelligent Vehicles, vol. 5, no. 3, pp. 508–518, 2020.

BibTeX

Toldo, Marco; Michieli, Umberto; Agresti, Gianluca; Zanuttigh, Pietro

Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment Journal Article

In: Image and Vision Computing, vol. 95, pp. 103889, 2020.

BibTeX

Toldo, Marco; Maracani, Andrea; Michieli, Umberto; Zanuttigh, Pietro

Unsupervised domain adaptation in semantic segmentation: a review Journal Article

In: Technologies, vol. 8, no. 2, pp. 35, 2020.

BibTeX