DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation

• URL: http://arxiv.org/abs/2312.04813v1
• Date: Fri, 8 Dec 2023 03:03:22 GMT
• Title: DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
• Authors: Haoran Fan, Qi Fan, Maurice Pagnucco, Yang Song
• Abstract summary: Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only a small number of supporting images from base classes. In cross-domain FSS, leveraging features from label-rich domains for resource-constrained domains poses challenges due to domain discrepancies. This work presents a Dynamically Adaptive Refine (DARNet) method that aims to balance generalization and specificity for CD-FSS.
• Score: 20.979759016826378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only a small number of supporting images from base classes. However, in cross-domain few-shot segmentation (CD-FSS), leveraging features from label-rich domains for resource-constrained domains poses challenges due to domain discrepancies. This work presents a Dynamically Adaptive Refine (DARNet) method that aims to balance generalization and specificity for CD-FSS. Our method includes the Channel Statistics Disruption (CSD) strategy, which perturbs feature channel statistics in the source domain, bolstering generalization to unknown target domains. Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy. We also present a Test-Time Adaptation (TTA) method to refine the model's adaptability to diverse feature distributions. Our approach demonstrates superior performance against state-of-the-art methods in CD-FSS tasks.

Related papers

• Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation [72.70876977882882]
  Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. We propose a novel method for Few-Shot Unsupervised Domain Adaptation (FSUDA), where only a limited number of unlabeled target domain samples are available for training.
  arXiv  Detail & Related papers  (2023-09-03T16:02:01Z)
• Adaptive Semantic Consistency for Cross-domain Few-shot Classification [27.176106714652327]
  Cross-domain few-shot classification (CD-FSC) aims to identify novel target classes with a few samples. We propose a simple plug-and-play Adaptive Semantic Consistency framework, which improves cross-domain robustness. The proposed ASC enables explicit transfer of source domain knowledge to prevent the model from overfitting the target domain.
  arXiv  Detail & Related papers  (2023-08-01T15:37:19Z)
• Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation [3.367755441623275]
  Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. We propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA) This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains.
  arXiv  Detail & Related papers  (2023-07-26T09:40:19Z)
• Conditional Support Alignment for Domain Adaptation with Label Shift [8.819673391477034]
  Unlabelled domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on labeled samples on the source domain and unsupervised ones in the target domain. We propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions.
  arXiv  Detail & Related papers  (2023-05-29T05:20:18Z)
• Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction [62.41511766918932]
  Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios. We propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagrees on the unlabelled target data.
  arXiv  Detail & Related papers  (2023-02-28T16:31:17Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment [32.77436219094282]
  SSDAS employs a few labeled target samples as anchors for adaptive and progressive feature alignment between labeled source samples and unlabeled target samples. In addition, we replace the dissimilar source features by high-confidence target features continuously during the iterative training process. Extensive experiments show the proposed SSDAS greatly outperforms a number of baselines.
  arXiv  Detail & Related papers  (2021-06-05T09:12:50Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation [10.417009344120917]
  We present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. We validated our method on two cardiac datasets by adapting from the annotated source domain to the unannotated target domain.
  arXiv  Detail & Related papers  (2021-03-15T08:59:44Z)
• 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)
• Towards Fair Cross-Domain Adaptation via Generative Learning [50.76694500782927]
  Domain Adaptation (DA) targets at adapting a model trained over the well-labeled source domain to the unlabeled target domain lying in different distributions. We develop a novel Generative Few-shot Cross-domain Adaptation (GFCA) algorithm for fair cross-domain classification.
  arXiv  Detail & Related papers  (2020-03-04T23:25:09Z)

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.