Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity

• URL: http://arxiv.org/abs/2603.26190v1
• Date: Fri, 27 Mar 2026 09:03:49 GMT
• Title: Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity
• Abstract summary: 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.
• Score: 8.16821029459195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning seeks to maintain stable adaptation under non-stationary environments, yet this problem becomes particularly challenging in object detection, where most existing methods implicitly assume relatively balanced visual conditions. In extreme-sparsity regimes, such as those observed in space-based resident space object (RSO) detection scenarios, foreground signals are overwhelmingly dominated by background observations. Under such conditions, we analytically demonstrate that background-driven gradients destabilize the feature backbone during sequential domain shifts, causing progressive representation drift. This exposes a structural limitation of continual learning approaches relying solely on output-level distillation, as they fail to preserve intermediate representation stability. To address this, we propose a dual-stage invariant continual learning framework via joint distillation, enforcing structural and semantic consistency on both backbone representations and detection predictions, respectively, thereby suppressing error propagation at its source while maintaining adaptability. Furthermore, to regulate gradient statistics under severe imbalance, we introduce a sparsity-aware data conditioning strategy combining patch-based sampling and distribution-aware augmentation. Experiments on a high-resolution space-based RSO detection dataset show consistent improvement over established continual object detection methods, achieving an absolute gain of +4.0 mAP under sequential domain shifts.

Related papers

• Making Every Step Count: Spatio-Temporal Information Allocation for Imaging Inverse Problems [63.904793414062816]
  We introduce two complementary and training-free components, i.e., Spectrum-based Scheduling (SAS) and Measurement-Prioritized Attention (MPA)<n>SAS distributes the available NFEs over flow time according to the spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement.<n>MPA guides information toward weakly constrained regions, thereby enhancing semantic and structural fidelity.
  arXiv  Detail & Related papers  (2026-08-12T07:39:22Z)
• Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration [15.371827475537776]
  Local Epistemic Uncertainty Guided Active Sampling framework is developed.<n>It balances detail preservation and artifact suppression in the spatial domain.<n>It reduces sampling time with negligible memory overhead.
  arXiv  Detail & Related papers  (2026-08-07T09:00:20Z)
• Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection [34.23489857539403]
  We propose a unified framework thatFocuses, Aligns, and Sustains gradient flow throughout incremental learning.<n>Experiments show that FAS restores robust optimization dynamics and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-06-13T11:20:16Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Continual Segmentation under Joint Nonstationarity [8.522964568257455]
  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.
  arXiv  Detail & Related papers  (2026-05-19T22:20:00Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation [51.56484100374058]
  We formalise such information-conditioned interaction patterns as behavioural dependency.<n>This induces a probe-relative notion of $$-behavioural equivalence and a within-policy behavioural distance.<n>Results identify structural conditions under which probe-conditioned behavioural separation is not preserved under common policy transformations.
  arXiv  Detail & Related papers  (2026-02-24T22:55:21Z)
• Binary Flow Matching: Prediction-Loss Space Alignment for Robust Learning [23.616336786063552]
  Flow matching has emerged as a powerful framework for generative modeling.<n>We identify a latent structural mismatch that arises when it is coupled with velocity-based objectives.<n>We prove that re-aligning the objective to the signal space eliminates the singular weighting.
  arXiv  Detail & Related papers  (2026-02-11T02:02:30Z)
• VSCOUT: A Hybrid Variational Autoencoder Approach to Outlier Detection in High-Dimensional Retrospective Monitoring [0.0]
  VSCOUT is a distribution-free framework designed for retrospective (Phase I) monitoring in high-dimensional settings.<n>VSCOUT achieves superior sensitivity to special-cause structure while maintaining controlled false alarms.<n>Its scalability, distributional flexibility, and resilience position VSCOUT as a practical and effective method for retrospective modeling and anomaly detection in AI-enabled environments.
  arXiv  Detail & Related papers  (2026-01-28T18:30:48Z)
• Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity [41.890949680958805]
  Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data.<n>Clients destabilize FL by distorting local gradient dynamics during client-side optimization.<n>We develop a general client-side perspective that regulates local gradient contributions without incurring additional communication overhead.
  arXiv  Detail & Related papers  (2026-01-07T04:58:18Z)
• Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning [5.309590159815129]
  We propose a novel end-to-end finite-horizon Wasserstein DRC framework.<n>It integrates the learning of anisotropic Wasserstein metrics with downstream control tasks in a closed-loop manner.<n>We show that the proposed framework achieves superior closed-loop performance and robustness compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-11T13:40:49Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations [67.35596444651037]
  Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable.<n>We propose a Reliable Test-time Adaptation (ReTA) method that enhances reliability from two perspectives.
  arXiv  Detail & Related papers  (2025-07-13T05:37:33Z)
• Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection [19.684132921720945]
  The balance between stability and plasticity remains a fundamental challenge in pretrained model-based incremental object detection.<n>We propose a dual-path framework built upon pretrained DETR-based detectors which decouples localization stability and classification plasticity.<n>We show our method's ability to effectively balance stability and plasticity in PTMIOD, achieving robust cross-domain adaptation and strong retention of anti-forgetting capabilities.
  arXiv  Detail & Related papers  (2025-04-14T13:31:35Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Beyond the Edge of Stability via Two-step Gradient Updates [49.03389279816152]
  Gradient Descent (GD) is a powerful workhorse of modern machine learning. GD's ability to find local minimisers is only guaranteed for losses with Lipschitz gradients. This work focuses on simple, yet representative, learning problems via analysis of two-step gradient updates.
  arXiv  Detail & Related papers  (2022-06-08T21:32:50Z)

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.