双阶段不变持续学习在极端视觉稀疏性下的应用
Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity
AI总结：
本文提出双阶段不变持续学习框架，通过联合蒸馏保持特征表示稳定性，解决极端稀疏条件下持续学习的挑战，实验表明在空间残留卫星检测中提升mAP 4.0。
摘要：
持续学习旨在非平稳环境中维持稳定适应，但在目标检测中尤为困难，因现有方法隐假设视觉条件平衡。在极端稀疏性条件下，如空间残留卫星检测，前景信号被背景观测主导。我们证明背景驱动梯度破坏特征主干，导致特征漂移。现有仅依赖输出级蒸馏的方法无法保持中间表示稳定性。为此，我们提出双阶段不变持续学习框架，通过联合蒸馏实现主干表示和检测预测的结构和语义一致性，抑制误差传播并保持适应性。此外，引入稀疏感知数据条件策略，结合基于块的采样和分布感知增强，以调节严重不平衡下的梯度统计。实验表明在高分辨率空间残留卫星检测数据集上，相比现有持续目标检测方法，连续领域转移中mAP绝对提升4.0。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
