解决看不见的关系:属性 文本中的相关性 属性 人类搜索
IEEE transactions on neural networks and learning systems
|August 11, 2023
概括
这项研究引入了一种新的图形卷积网络 (GCN) 用于文本属性人搜索,使用目击者描述改进行人识别. 该方法有效地模拟了属性相关性,优于对基准数据集的现有方法.
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:
- 文本属性人搜索通过文本描述识别行人,当图像样本不可用时至关重要.
- 现有的方法往往忽略了属性之间的潜在相关性,从而限制了性能.
- 开发可靠的基于文本的身份识别方法对于监视和安全至关重要.
研究的目的:
- 提出一个新的图形卷积网络 (GCN) 和基于伪标签的方法用于文本属性人搜索.
- 为了有效地建模和利用文本属性之间的潜在相关性.
- 提高从文字描述中识别人员的准确性和稳定性.
主要方法:
- 使用标签同时发生的概率通过GCN构建属性相关性.
- 将属性表示为节点,并将它们的相关性表示为图的边缘.
- 将交叉注意模块 (CAM) 与 GCN 结合起来,以实现增强的表示.
- 在测试集上使用伪标签来适应看不见的属性关系.
主要成果:
- 拟议的基于GCN的方法显著优于最先进的方法.
- 有效的属性相关性建模导致了人搜索准确度的提高.
- 该模型在处理看不见的属性关系方面表现出了稳健性.
结论:
- 开发的GCN和伪标签方法在文本属性人搜索方面取得了重大进展.
- 利用属性相关性和适应性学习可以提高识别性能.
- 这种方法为仅依赖于证人讲述的个人身份识别场景提供了更实用的解决方案.
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