对资源有限的设备进行了优化的端到端人类分析和检测
Md Imran Hosen1,2, Tarkan Aydin3, Md Baharul Islam3,4,5
1Department of Computer Engineering, Bahcesehir University, Yildiz, Ciragan Cd, Besiktas, 34349, Istanbul, Turkey. mdimran.hosen@bau.edu.tr.
Scientific reports
|December 10, 2025
概括
我们开发了一个高效的人类解析框架,使用自我注意力来更好地理解语境. 这种方法优化了低资源设备,在一次通过中实现快速和准确的结果.
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:
- 人类解析对于以人为中心的分析至关重要,对服装和身体部位进行细分.
- 当前的方法往往需要辅助输入,阻碍了对资源有限的设备的使用.
研究的目的:
- 为低资源环境优化的人类解析提出一个端到端的框架.
- 为了增强上下文理解,并使同时检测和解析.
主要方法:
- 实现了基于变压器的自我注意模块,以改善上下文信息.
- 引入了边界多边形注释,用于集成检测和解析.
- 优化了对资源有限的硬件进行高效推断的框架.
主要成果:
- 实现了细粒度的人类解析结果,在单个推理通过.
- 显著提高了推断速度,而不会影响准确度.
- 在Raspberry Pi上证明了有效性和效率,用于现实世界的验证.
结论:
- 拟议的框架提供了一个高效和准确的解决方案,用于在资源有限的环境中进行人类分析.
- 自我注意和边界多边形注释的集成提升了单通的人类解析能力.
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