Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection
Journal:
arXiv
Published Date:
Apr 14, 2025
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.
