高级交互和波形卷积网络用于可见红外人重新识别
Li Ma1, Rui Kong2, XinGuan Dai1
1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Scientific reports
|August 21, 2025
概括
这项研究引入了高级交互和波形卷积网络 (HIW-Net),以改善可见红外人重新识别 (VI-ReID). HIW-Net有效地整合了原始特征,并使用波形卷积来提高跨模式重新识别任务的性能.
科学领域:
- 计算机视觉
- 人工智能
- 机器学习
背景情况:
- 可见红外人重新识别 (VI-ReID) 面临跨模式差异和图像质量低下的挑战.
- 当前的深度学习方法在高层次抽象过程中经常失去关键的原始特征.
研究的目的:
- 提出高级交互和波形卷积网络 (HIW-Net),以解决VI-ReID中的信息丢失问题.
- 通过整合原始特征和使用波形卷积来增强特征提取.
主要方法:
- 开发了HIW-Net,在多个交互阶段整合了原始特征.
- 用波形卷积来进行多种特征挖掘和改进特征提取.
- 使用分段任何模型 (SAM) 创建RegDB_shape数据集以进行训练增强.
主要成果:
- 与最先进的方法相比,HIW-Net在SYSU-MM01和RegDB数据集上表现出更高的性能.
- 建议的方法有效地弥补了高级表示中的信息丢失.
- 波形卷积有助于更全面的特征提取.
结论:
- HIW-Net在可见红外人重新识别方面取得了重大进展.
- 原始特征和波形卷积的整合证明对交叉模式差异有效.
- RegDB_shape数据集有助于改进VI-ReID模型培训.
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