Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection

• URL: http://arxiv.org/abs/2504.10214v1
• Date: Mon, 14 Apr 2025 13:31:35 GMT
• Title: Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection
• Abstract summary: The balance between stability and plasticity remains a fundamental challenge in pretrained model-based incremental object detection.<n>We propose a dual-path framework built upon pretrained DETR-based detectors which decouples localization stability and classification plasticity.<n>We show our method's ability to effectively balance stability and plasticity in PTMIOD, achieving robust cross-domain adaptation and strong retention of anti-forgetting capabilities.
• Score: 19.684132921720945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The balance between stability and plasticity remains a fundamental challenge in pretrained model-based incremental object detection (PTMIOD). While existing PTMIOD methods demonstrate strong performance on in-domain tasks aligned with pretraining data, their plasticity to cross-domain scenarios remains underexplored. Through systematic component-wise analysis of pretrained detectors, we reveal a fundamental discrepancy: the localization modules demonstrate inherent cross-domain stability-preserving precise bounding box estimation across distribution shifts-while the classification components require enhanced plasticity to mitigate discriminability degradation in cross-domain scenarios. Motivated by these findings, we propose a dual-path framework built upon pretrained DETR-based detectors which decouples localization stability and classification plasticity: the localization path maintains stability to preserve pretrained localization knowledge, while the classification path facilitates plasticity via parameter-efficient fine-tuning and resists forgetting with pseudo-feature replay. Extensive evaluations on both in-domain (MS COCO and PASCAL VOC) and cross-domain (TT100K) benchmarks show state-of-the-art performance, demonstrating our method's ability to effectively balance stability and plasticity in PTMIOD, achieving robust cross-domain adaptation and strong retention of anti-forgetting capabilities.

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