随机风格扰动建模可见红外人重新识别与严重的模态失衡
Haojie Liu1, Zhiyong Li2, Jianyang Gu2
1The College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China; The School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China.
概括
本研究引入了共模式平衡学习 (CMBL) 框架,以解决可见红外人重新识别 (VI-ReID) 中的数据不平衡问题. 在不同的视觉条件下,CMBL有效地增强了特征提取,以便在不同的视觉条件下进行更强大的人身识别.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 可见红外人重新识别 (VI-ReID) 面临着重大挑战,因为可见 (RGB) 和红外 (IR) 模式之间的数据不平衡.
- 有限的IR数据,通常是由于成本和运营限制造成的,导致模型培训期间的代表性不足,阻碍了性能.
研究的目的:
- 开发一个新的框架,共同模式平衡学习 (CMBL),以有效地解决VI-ReID中的数据不平衡.
- 为了提高不同视觉模式的人重新识别的准确性和稳定性.
主要方法:
- 引入了一个随机风格扰动 (SSP) 模块来生成合成IR样本,平衡数据集.
- 开发了交叉分布对齐损失,以精细优化稀疏模式特征.
- 拟议的类意识对比相似性学习 (CACS) 增强特征可歧视性.
主要成果:
- 在不平衡的条件下,CMBL框架显示了公共VI-ReID数据集的显著改进.
- 废弃性研究证实了单个成分 (SSP,交叉分布损失,CACS) 在减轻数据失衡影响方面的有效性.
- 这种方法成功地改善了类内紧性和类间隔离.
结论:
- 拟议的CMBL框架有效地解决了VI-ReID中的数据不平衡.
- 这些方法增强了特征提取,导致更准确和更强大的跨模式人重新识别.
- 这项工作为具有固有的数据差异的真实世界VI-ReID应用提供了有价值的解决方案.
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