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

Source: [Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection](<https://www.sota2.com/research/paper/68fa780e1e209131cea7692b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:31:26.317Z

Authors: Songze Li, Qixing Xu, Tonghua Su, Xu-Yao Zhang, Zhongjie Wang

Published: 2025-04-14T13:31:35.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.10214>)

[PDF](<https://arxiv.org/pdf/2504.10214.pdf>)