Continual Segmentation under Joint Nonstationarity

• URL: http://arxiv.org/abs/2605.20538v1
• Date: Tue, 19 May 2026 22:20:00 GMT
• Title: Continual Segmentation under Joint Nonstationarity
• Abstract summary: Evolving data streams induce joint nonstationarity in continual semantic segmentation.<n>This setting reflects practical structured prediction systems, yet remains largely unexplored in prior continual learning work.<n>We formalize continual segmentation under coupled class, domain, and label shifts and investigate learning in heterogeneous dense prediction environments.
• Score: 8.522964568257455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evolving data streams induce joint nonstationarity in continual semantic segmentation, where semantic classes, input distributions, and supervision availability change simultaneously over time. This setting reflects practical structured prediction systems, yet remains largely unexplored in prior continual learning work, which typically studies these factors in isolation. We formalize continual segmentation under coupled class, domain, and label shifts and investigate learning in heterogeneous dense prediction environments with limited annotations and abundant unlabeled data. To address instability and overfitting arising from few-shot supervision under distribution drift, we introduce gradient-adaptive stabilization, a parameter-wise regularization mechanism implemented via gradient-scaled stochastic perturbations that promotes a principled stability-plasticity tradeoff. We further leverage unlabeled data through semi-supervised learning and introduce prototype anchored supervision that validates pseudo-labels via joint confidence and prototype consistency. Together, these mechanisms enable learning under joint nonstationarity in continual segmentation. Extensive empirical evaluation across class-incremental, domain-incremental, and few-shot regimes demonstrates consistent improvements over prior methods in heterogeneous structured prediction settings. Our results expose fundamental failure modes of existing continual segmentation approaches and provide insight into learning robust dense predictors in dynamically evolving environments.

Related papers

• SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation [65.81476183180527]
  We introduce SegDiff, a closed-loop visuomotor policy that integrates the strengths of both paradigms.<n>SegDiff decomposes demonstrations into motion segments between keyposes and learns to predict the continuous trajectory from the current state to the next keypose.<n>SegDiff demonstrates significant performance gains over existing approaches across various simulated and real-world scenarios.
  arXiv  Detail & Related papers  (2026-07-13T02:48:03Z)
• Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting [64.2640656619583]
  Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction.<n>We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting.
  arXiv  Detail & Related papers  (2026-05-24T07:31:55Z)
• Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging [65.11234199720428]
  Continual Test-Time Adaptation aims to empower perception systems to handle dynamic distribution shifts encountered after deployment.<n>We introduce a novel forward-facilitation paradigm through a method termed Dynamic Style Bridging.<n>During test time, to mitigate inherent generative bias and adapt these proxies to incoming data, we propose a multi-level bridging mechanism.
  arXiv  Detail & Related papers  (2026-05-18T16:18:17Z)
• Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions [0.0]
  This paper introduces Anticipatory Reinforcement Learning (ARL), a novel framework designed to bridge the gap between non-Markovian decision processes and classical reinforcement learning architectures.<n>In environments characterised by jump-diffusions and structural breaks, traditional state-based methods often fail to capture the essential path-dependent geometry required for accurate foresight.
  arXiv  Detail & Related papers  (2026-04-06T13:15:44Z)
• Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity [8.16821029459195]
  We show that background-driven gradients destabilize the feature backbone during sequential domain shifts.<n>We propose a dual-stage invariant continual learning framework via joint distillation.<n> Experiments on a high-resolution space-based RSO detection dataset show consistent improvement over established continual object detection methods.
  arXiv  Detail & Related papers  (2026-03-27T09:03:49Z)
• CURVE: Learning Causality-Inspired Invariant Representations for Robust Scene Understanding via Uncertainty-Guided Regularization [30.613712415224473]
  CURVE is a framework that integrates variational uncertainty modeling with uncertainty-guided structural regularization to suppress high-variance relations.<n>Specifically, we apply prototype-conditioned debiasing to disentangle invariant interaction dynamics from environment-dependent variations, promoting a sparse and domain-stable topology.<n> Empirically, we evaluate CURVE in zero-shot transfer and low-data sim-to-real adaptation, verifying its ability to learn domain-stable sparse topologies and provide reliable uncertainty estimates to support risk prediction under distribution shifts.
  arXiv  Detail & Related papers  (2026-01-28T08:15:56Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data [49.36938105983916]
  Unsupervised domain adaptation methods seek to generalize effectively on unlabeled test data.<n>We propose incorporating multi-scale feature extraction and uncertainty estimation to improve the model's generalization and robustness across domains.
  arXiv  Detail & Related papers  (2025-08-26T03:13:08Z)
• FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation [26.585985828583304]
  Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data.<n>Current approaches often discard uncertain regions through strict thresholding favouring dominant classes.<n>We introduce a holistic framework that transforms uncertainty into a learning asset through four principal components.
  arXiv  Detail & Related papers  (2025-06-11T03:47:08Z)
• Which Invariance Should We Transfer? A Causal Minimax Learning Approach [18.71316951734806]
  We present a comprehensive minimax analysis from a causal perspective. We propose an efficient algorithm to search for the subset with minimal worst-case risk. The effectiveness and efficiency of our methods are demonstrated on synthetic data and the diagnosis of Alzheimer's disease.
  arXiv  Detail & Related papers  (2021-07-05T09:07:29Z)
• Discrete Variational Attention Models for Language Generation [51.88612022940496]
  We propose a discrete variational attention model with categorical distribution over the attention mechanism owing to the discrete nature in languages. Thanks to the property of discreteness, the training of our proposed approach does not suffer from posterior collapse.
  arXiv  Detail & Related papers  (2020-04-21T05:49:04Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.