Adapting Detection Transformers to Incremental Object Detection (IOD) poses a systemic challenge, as set-based optimization is inherently destabilized by sequential learning. In this work, we identify Gradient Dilution as the root cause of performance degradation, wherein optimization signals required to preserve old knowledge are progressively weakened. This phenomenon manifests as a cascading erosion of preservation gradients in magnitude, direction, and support coverage, driven by three tightly coupled factors: Signal Dispersion, where foreground gradients are overwhelmed by background noise; Assignment Drift, where stochastic query-target matching induces inconsistent gradient trajectories; and Support Attrition, where gradients from retained samples insufficiently cover the old-class feature space, weakening decision boundaries under interference from new classes. To counteract this, we propose FAS, a unified framework that Focuses, Aligns, and Sustains gradient flow throughout incremental learning. Specifically, we introduce prior-injected queries to focus discriminative signals by filtering background interference at the source. We further propose deterministic anchor distillation to align query-target assignments and enforce semantic consistency across stages under unstable matching. Finally, we devise manifold-support replay to sustain distributional support of old classes, counteracting representational erosion induced by continual updates. Extensive experiments show that FAS restores robust optimization dynamics and outperforms state-of-the-art methods, achieving over 5.0 AP improvement in the challenging 40+10x4 incremental setting.

翻译：将检测Transformer适配至增量目标检测面临系统性挑战，基于集合的优化本身会因顺序学习而失稳。本研究识别出梯度稀释是性能退化的根本原因——用于保留旧知识的优化信号逐渐衰减。该现象表现为保留梯度在幅值、方向和支持覆盖范围上的级联式侵蚀，由三个紧密耦合的因素驱动：信号弥散（前景梯度被背景噪声淹没）、分配漂移（随机查询-目标匹配导致梯度轨迹不一致）以及支持衰减（保留样本的梯度不足以覆盖旧类别特征空间，在新类别干扰下弱化决策边界）。为应对该问题，我们提出FAS统一框架，在增量学习过程中聚焦、对齐并维持梯度流。具体而言，我们引入先验注入查询以源头过滤背景干扰，聚焦判别性信号；进而提出确定性锚点蒸馏以对齐查询-目标分配，在不稳定匹配下跨阶段强制语义一致性；最后设计流形支持回放以维持旧类别的分布支持，抵御持续更新引起的表征侵蚀。大量实验表明，FAS恢复了稳健的优化动态并超越现有方法，在40+10x4挑战性增量设定中实现超过5.0 AP的提升。