SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation

• URL: http://arxiv.org/abs/2405.14278v1
• Date: Thu, 23 May 2024 07:53:10 GMT
• Title: SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation
• Authors: Kai Yao, Zhaorui Tan, Zixian Su, Xi Yang, Jie Sun, Kaizhu Huang,
• Abstract summary: Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open unseen domains. Existing OCDA methods solve the intra-domain gaps by a divide-and-conquer strategy, which divides the problem into several individual and parallel domain adaptation (DA) tasks. We present Compound Mixing (SCMix), an augmentation strategy with the primary objective of mitigating the divergence between source and mixed target distributions.
• Score: 20.311948275950606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open unseen domains. Existing OCDA methods solve the intra-domain gaps by a divide-and-conquer strategy, which divides the problem into several individual and parallel domain adaptation (DA) tasks. Such approaches often contain multiple sub-networks or stages, which may constrain the model's performance. In this work, starting from the general DA theory, we establish the generalization bound for the setting of OCDA. Built upon this, we argue that conventional OCDA approaches may substantially underestimate the inherent variance inside the compound target domains for model generalization. We subsequently present Stochastic Compound Mixing (SCMix), an augmentation strategy with the primary objective of mitigating the divergence between source and mixed target distributions. We provide theoretical analysis to substantiate the superiority of SCMix and prove that the previous methods are sub-groups of our methods. Extensive experiments show that our method attains a lower empirical risk on OCDA semantic segmentation tasks, thus supporting our theories. Combining the transformer architecture, SCMix achieves a notable performance boost compared to the SoTA results.

Related papers

• Domain Generalisation via Risk Distribution Matching [17.334794920092577]
  We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains. In testing, we may observe similar, or potentially intensifying in magnitude, divergences between risk distributions. We show that Risk Distribution Matching (RDM) shows superior generalisation capability over state-of-the-art DG methods.
  arXiv  Detail & Related papers  (2023-10-28T05:23:55Z)
• CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation [20.589323508870592]
  Semi-supervised domain adaptation (SSDA) adapts a learner to a new domain by effectively utilizing source domain data and a few labeled target samples. We show that the proposed model significantly outperforms SOTA methods in terms of effectiveness and generalisability on SSDA datasets.
  arXiv  Detail & Related papers  (2023-03-30T16:48:28Z)
• Compound Domain Generalization via Meta-Knowledge Encoding [55.22920476224671]
  We introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions. We harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space. Experiments on four standard Domain Generalization benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision.
  arXiv  Detail & Related papers  (2022-03-24T11:54:59Z)
• Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain [0.0]
  Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. We propose a novel MDA approach, termed Pseudo Target for MDA (PTMDA) PTMDA maps each group of source and target domains into a group-specific subspace using adversarial learning with a metric constraint. We show that PTMDA as a whole can reduce the target error bound and leads to a better approximation of the target risk in MDA settings.
  arXiv  Detail & Related papers  (2022-02-22T08:37:16Z)
• Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation [12.703234995718372]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain. Access to the source domain data at the adaptation stage is often limited, due to data storage or privacy issues. We propose to adapt an off-the-shelf" segmentation model pre-trained in the source domain to the target domain.
  arXiv  Detail & Related papers  (2021-06-23T16:16:55Z)
• Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing [53.82826073959756]
  Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. We propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels. To overcome the limitation, we propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels.
  arXiv  Detail & Related papers  (2021-05-06T06:04:59Z)
• Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation [51.93711960601973]
  We propose a novel margin preserving self-paced contrastive Learning model for cross-modal medical image segmentation. With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space. Experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance.
  arXiv  Detail & Related papers  (2021-03-15T15:23:10Z)
• Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation [102.42638795864178]
  We propose a principled meta-learning based approach to OCDA for semantic segmentation. We cluster target domain into multiple sub-target domains by image styles, extracted in an unsupervised manner. A meta-learner is thereafter deployed to learn to fuse sub-target domain-specific predictions, conditioned upon the style code. We learn to online update the model by model-agnostic meta-learning (MAML) algorithm, thus to further improve generalization.
  arXiv  Detail & Related papers  (2020-12-15T13:21:54Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)

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.