通过动态的知识适应和保留来进行终身人重新识别
Zhiyu Chen1, Bingliang Jiao1, Wenxuan Wang2
1Department of Computer Science, Northwestern Polytechnical University, No. 127 Youyi West Road, Beilin District, Xi'an, Shaanxi Province, China.
概括
这项研究引入了终身人重新识别 (ReID) 的新框架,以防止知识丢失. 该方法在保持旧知识的同时动态适应新数据,在持续学习场景中显著提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 个人重新识别 (ReID) 模型需要持续学习,以便在现实世界中应用.
- 终身ReID面临着灾难性的遗忘,阻碍了对新数据的性能.
- 现有的方法很难在旧和新领域之间平衡性能.
研究的目的:
- 为终身ReID提出一个动态知识适应和保留框架.
- 解决现有方法在旧和新领域之间平衡性能方面的局限性.
- 为了减轻灾难性遗忘,同时能够适应新数据.
主要方法:
- 一个共享的骨干网络提取功能,其次是动态适应模块,例如规范化和自适应内核生成.
- 适应性保留策略限制了使用结模型作为知识的模型更新.
- 约束损失保持了知识和可训练模型之间的参数一致性.
主要成果:
- 拟议的框架实现了动态学习和适应性特征优化.
- 它有效地平衡了知识的保留和适应新领域.
- 在终身设置中,在四个主流ReID数据集上表现出色.
结论:
- 动态知识适应和保留框架成功地减轻了终身ReID中的灾难性遗忘.
- 该方法实现了卓越的性能,平均排名-1精度为65.7%,mAP为55.4%.
- 这种方法为个人再识别系统的持续学习提供了强大的解决方案.
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