Computer Science > Computer Vision and Pattern Recognition
[Submitted on 8 Feb 2025 (v1), last revised 29 May 2025 (this version, v3)]
Title:Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
View PDF HTML (experimental)Abstract:Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or knowledge distillation without analyzing component-specific forgetting. Through dissection of Faster R-CNN, we reveal a key insight: Catastrophic forgetting is predominantly localized to the RoI Head classifier, while regressors retain robustness across incremental stages. This finding challenges conventional assumptions, motivating us to develop a framework termed NSGP-RePRE. Regional Prototype Replay (RePRE) mitigates classifier forgetting via replay of two types of prototypes: coarse prototypes represent class-wise semantic centers of RoI features, while fine-grained prototypes model intra-class variations. Null Space Gradient Projection (NSGP) is further introduced to eliminate prototype-feature misalignment by updating the feature extractor in directions orthogonal to subspace of old inputs via gradient projection, aligning RePRE with incremental learning dynamics. Our simple yet effective design allows NSGP-RePRE to achieve state-of-the-art performance on the Pascal VOC and MS COCO datasets under various settings. Our work not only advances IOD methodology but also provide pivotal insights for catastrophic forgetting mitigation in IOD. Code is available at \href{this https URL}{this https URL} .
Submission history
From: Qirui Wu [view email][v1] Sat, 8 Feb 2025 12:10:02 UTC (811 KB)
[v2] Mon, 17 Feb 2025 12:36:11 UTC (378 KB)
[v3] Thu, 29 May 2025 07:12:52 UTC (222 KB)
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