基于图形采样的多流增强网络可见红外人重新识别
Jinhua Jiang1, Junjie Xiao1, Renlin Wang2
1College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一个新的可见红外人重新识别 (VI Re-ID) 网络,该网络使用轮信息和图形采样来提高准确性. 基于图表采样的多流增强网络 (GSMEN) 有效地减少了模式差异,以更好地匹配人.
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:
- 单模个人重新识别 (Re-ID) 不足以满足全天的检索需求.
- 多模式数据,特别是可见红外线人重新识别 (VI Re-ID),至关重要,但受到重大模式差异的挑战.
- 现有的方法往往忽略了人的轮信息的模式不变性.
研究的目的:
- 解决VI Re-ID中的挑战,特别是模式差异和区分硬样本.
- 提出一个新的网络,利用轮信息和先进的采样技术来改善跨模式匹配.
- 为了提高人匹配在可见和红外模式的稳定性和准确性.
主要方法:
- 提出了基于图形采样的多流增强网络 (GSMEN),其中包括一个轮扩展模块 (CEM).
- CEM集成了人形轮信息,以减少模式差异,增强匹配稳定性.
- 引入了交叉模式图谱采样器 (CGS) 进行智能样本选择,将类似的交叉模式样本分组起来,以探索硬类边界.
主要成果:
- 对SYSU-MM01和RegDB数据集的实验证明了拟议的GSMEN的优越性.
- 在RegDB数据集上的VIS→IR任务中实现了93.69%的Rank-1和92.56%的mAP.
- 该方法有效地减少了模式差异,并改善了硬交叉模式样本的区分.
结论:
- 拟议的GSMEN通过利用轮信息有效地解决了VI Re-ID中的模式差异挑战.
- 交叉模式图表采样器通过专注于困难的交叉模式样本对来帮助培训.
- 该方法显示了显著的性能改进,突出了VI Re-ID.中的轮和智能采样的重要性.
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