Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals

• URL: http://arxiv.org/abs/2509.01299v1
• Date: Mon, 01 Sep 2025 09:35:47 GMT
• Title: Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals
• Authors: Huan Ni, Qingshan Liu, Xiaonan Niu, Danfeng Hong, Lingli Zhao, Haiyan Guan,
• Abstract summary: Cross-domain few-shot segmentation enables the segmentation of unseen categories with very limited samples.<n>Existing CD-FSS studies typically design multiple independent modules to enhance the cross-domain generalization ability.<n>This paper proposes an all-in-one module based on ordinary differential equations and transforms.
• Score: 24.440387232889844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain few-shot segmentation (CD-FSS) not only enables the segmentation of unseen categories with very limited samples, but also improves cross-domain generalization ability within the few-shot segmentation framework. Currently, existing CD-FSS studies typically design multiple independent modules to enhance the cross-domain generalization ability of feature representations. However, the independence among these modules hinders the effective flow of knowledge, making it difficult to fully leverage their collective potential. In contrast, this paper proposes an all-in-one module based on ordinary differential equations and Fourier transform, resulting in a structurally concise method--Few-Shot Segmentation over Time Intervals (FSS-TIs). FSS-TIs assumes the existence of an ODE relationship between the spectra (including amplitude and phase spectra) of domain-specific features and domain-agnostic features. This ODE formulation yields an iterative transformation process along a sequence of time intervals, while simultaneously applying affine transformations with randomized perturbations to the spectra. In doing so, the exploration of domain-agnostic feature representation spaces and the simulation of diverse potential target-domain distributions are reformulated as an optimization process over the intrinsic parameters of the ODE. Moreover, we strictly constrain the support-sample selection during target-domain fine-tuning so that it is consistent with the requirements of real-world few-shot segmentation tasks. For evaluation, we introduce five datasets from substantially different domains and define two sets of cross-domain few-shot segmentation tasks to comprehensively analyze the performance of FSS-TIs. Experimental results demonstrate the superiority of FSS-TIs over existing CD-FSS methods, and in-depth ablation studies further validate the cross-domain adaptability of FSS-TIs.

Related papers

• Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification [5.784164305429653]
  This paper proposes a self-supervised cross-domain transfer framework.<n>It learns transferable spectral-spatial joint representations without source labels.<n> Experimental results demonstrate stable classification performance and strong cross-domain adaptability.
  arXiv  Detail & Related papers  (2026-01-26T02:52:35Z)
• UniDiff: A Unified Diffusion Framework for Multimodal Time Series Forecasting [90.47915032778366]
  We propose UniDiff, a unified diffusion framework for multimodal time series forecasting.<n>At its core lies a unified and parallel fusion module, where a single cross-attention mechanism integrates structural information from timestamps and semantic context from texts.<n>Experiments on real-world benchmark datasets across eight domains demonstrate that the proposed UniDiff model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-12-08T05:36:14Z)
• FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection [46.14695068852788]
  Single Domain Generalization for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains.<n>We propose a novel framework that enhances object detection by integrating the von Mises-Fisher (vMF) distribution and Fourier transformation into a CLIP-guided pipeline.<n>Our method not only preserves the semantic alignment benefits of CLIP but also enriches feature diversity and structural consistency across domains.
  arXiv  Detail & Related papers  (2025-11-13T14:28:10Z)
• OptiCorNet: Optimizing Sequence-Based Context Correlation for Visual Place Recognition [2.3093110834423616]
  This paper presents OptiCorNet, a novel sequence modeling framework.<n>It unifies spatial feature extraction and temporal differencing into a differentiable, end-to-end trainable module.<n>Our approach outperforms state-of-the-art baselines under challenging seasonal and viewpoint variations.
  arXiv  Detail & Related papers  (2025-07-19T04:29:43Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series [24.307819352969037]
  We introduce CICADA (Cross-domain Interpretable Coding for Anomaly Detection and Adaptation), with four key innovations.<n> CICADA captures domain-agnostic anomaly features with high flexibility and interpretability.<n>Trials on synthetic and real-world industrial datasets demonstrate that CICADA outperforms state-of-the-art methods in both cross-domain detection performance and interpretability.
  arXiv  Detail & Related papers  (2025-05-01T09:26:40Z)
• Adapting In-Domain Few-Shot Segmentation to New Domains without Retraining [53.963279865355105]
  Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains.<n>Most CD-FSS methods redesign and retrain in-domain FSS models using various domain-generalization techniques.<n>We propose adapting informative model structures of the well-trained FSS model for target domains by learning domain characteristics from few-shot labeled support samples.
  arXiv  Detail & Related papers  (2025-04-30T08:16:33Z)
• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• Integrating Frequency Guidance into Multi-source Domain Generalization for Bearing Fault Diagnosis [24.85752780864944]
  We propose the Fourier-based Augmentation Reconstruction Network, namely FARNet.<n>The network comprises an amplitude spectrum sub-network and a phase spectrum sub-network, sequentially reducing the discrepancy between the source and target domains.<n>To refine the decision boundary of our model output compared to conventional triplet loss, we propose a manifold triplet loss to contribute to generalization.
  arXiv  Detail & Related papers  (2025-02-01T20:23:03Z)
• DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation [20.979759016826378]
  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.
  arXiv  Detail & Related papers  (2023-12-08T03:03:22Z)
• Unified Domain Adaptive Semantic Segmentation [96.74199626935294]
  Unsupervised Adaptive Domain Semantic (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain.<n>We propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies.<n>Our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T09:18:49Z)
• UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting [59.11817101030137]
  This research advocates for a unified model paradigm that transcends domain boundaries. Learning an effective cross-domain model presents the following challenges. We propose UniTime for effective cross-domain time series learning.
  arXiv  Detail & Related papers  (2023-10-15T06:30:22Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)

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.