GCReID:通过超级学习和知识积累进行通用的持续人重新识别
Zhaoshuo Liu1, Chaolu Feng2, Kun Yu3
1School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, Liaoning, China.
概括
本研究介绍了一种通用连续人重新识别 (GCReID) 模型,以防止灾难性遗忘和改善通用化. 通过模拟多样样样本和利用元学习策略,GCReID模型提高了未见领域的性能.
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:
- 人重新识别 (ReID) 模型在适应新领域时,与灾难性遗忘作斗争.
- 由于领域分布的差异,ReID现有的持续学习方法的概括性有限.
研究的目的:
- 为抗遗忘和可通用的ReID提出一个通用的持续人重新识别 (GCReID) 模型.
- 通过模拟未见的领域和使用元学习来增强模型概括性.
主要方法:
- GCReID模型模拟未见的域,以增加样本的多样性.
- 超级培训和超级测试策略用于促进概括.
- 一个图表注意力网络整合了来自可见和模拟领域的通用知识.
主要成果:
- 拟议的GCReID模型有效地缓解了被视域中的灾难性遗忘.
- 对12个基准数据集的实验显示,对未见域的概括有显著的改进.
- 在广泛的比较中,GCReID的表现优于6个代表性的ReID模型.
结论:
- GCReID模型提供了一个强大的解决方案,用于在人身重新识别中持续学习.
- 该方法成功地平衡了抗遗忘能力与在不同领域的增强泛化.
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