不重训练模型，用记忆检索实现目标检测的在线自适应
Online Learning via Memory: Retrieval-Augmented Detector Adaptation
- 测试时通过记忆库检索相似物体，动态增强检测能力
- 仅需每类10张图的记忆库，即可显著超越基线表现
- 适合快速适配新场景的检测系统，无需重新训练
本文提出一种无需重训练即可将现成目标检测模型在线适配到新领域的全新方法。受人类快速记忆新知识的启发，我们在测试阶段让检测器从记忆库中检索相似物体概念。通过检索增强分类（RAC）模块与可灵活更新的记忆库实现该机制。实验涵盖多种开集与闭集检测器。仅需每类别10张图像的记忆库，且完全无需训练，该方法在适应新领域时显著优于基线。
原文摘要 · Abstract (English)
This paper presents a novel way of online adapting any off-the-shelf object detection model to a novel domain without retraining the detector model. Inspired by how humans quickly learn knowledge of a new subject (e.g., memorization), we allow the detector to look up similar object concepts from memory during test time. This is achieved through a retrieval augmented classification (RAC) module together with a memory bank that can be flexibly updated with new domain knowledge. We experimented with various off-the-shelf open-set detector and close-set detectors. With only a tiny memory bank (e.g., 10 images per category) and being training-free, our online learning method could significantly outperform baselines in adapting a detector to novel domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。