Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection
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 traject...
| Authors | Aoting Zhang, Dongbao Yang, Chang Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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