对无监督人员重新识别的抗遗忘适应.
IEEE transactions on pattern analysis and machine intelligence
|November 4, 2024
概括
本研究引入了一个双层联合适应和抗遗忘 (DJAA) 框架,用于无监督人员重新识别 (ReID). DJAA使模型能够学习新的领域,而不会忘记以前的知识,从而提高了概括性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对人重新识别 (ReID) 的无监督域调整与知识保留和对新数据集的概括斗争.
- 现有的ReID模型在适应新领域时,往往会忘记先前获得的知识.
研究的目的:
- 提出一个新的框架,双层联合适应和抗遗忘 (DJAA),用于增量无监督域适应人ReID.
- 为了使ReID模型能够适应新的领域,同时保留来自源和先前适应的目标领域的知识.
主要方法:
- DJAA框架使用原型和实例级一致性进行适应.
- 一个内存缓冲区存储代表性图像样本和集群原型,逐渐更新.
- 规范化技术强制执行图像对图像和图像对原型的相似性,以练习过去的知识.
主要成果:
- 拟议的DJAA框架显著提高了无监督人ReID的抗遗忘能力.
- 实验表明,经过多步调整后,对未见的领域的概括能力得到了改善.
- 该方法在所有已见域中展示了强大的向后兼容性性能.
结论:
- DJAA框架有效地解决了增量无监督域适应性人ReID的忘记问题.
- 在不断变化的数据集中,DJAA为维护和改进ReID模型性能提供了一个强大的解决方案.
- 这种方法通过确保持续学习和广泛适用性来推进领域适应性人ReID的最新技术.
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