Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity

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• Existing continual object detection methods implicitly assume relatively balanced visual conditions, which is a limitation in extreme-sparsity regimes.limitation 90%
• Background-driven gradients destabilize the feature backbone during sequential domain shifts in extreme-sparsity regimes, causing progressive representation drift.technical 90%
• Continual learning approaches relying solely on output-level distillation fail to preserve intermediate representation stability under extreme visual sparsity.limitation 90%
• The proposed dual-stage invariant continual learning framework enforces structural and semantic consistency on backbone representations and detection predictions, respectively.method 95%
• A sparsity-aware data conditioning strategy combining patch-based sampling and distribution-aware augmentation is introduced to regulate gradient statistics under severe imbalance.method 95%
• The proposed method achieves an absolute gain of +4.0 mAP under sequential domain shifts on a high-resolution space-based RSO detection dataset.result 90%
• Replay-based and parameter-isolation continual object detection methods are structurally fragile under background-dominated sparsity due to a lack of informative samples.limitation 85%

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