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适应性稀疏的基于注意力的紧变压器用于对象跟踪.

Fei Pan1, Lianyu Zhao1, Chenglin Wang2

  • 1School of Computer Science and Engineering, Tianjin University of Technology, Liqizhaung street, Tianjin, 300384, China.

Scientific reports
|May 28, 2024
PubMed
概括
此摘要是机器生成的。

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本研究介绍了ASACTT,这是一种新的对象跟踪方法,可以增强全球信息捕获和特定目标的注意力. ASACTT实现了最先进的性能,提高了对象跟踪的效率和稳定性.

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:

  • 基于变压器的罗网络对于对象跟踪是有效的,但由于ResNet的骨干,它在全球信息捕获和特征表示方面遇到了困难.
  • 现有的方法在使用多头自我注意 (MSA) 来处理与目标相关的信息方面面临挑战,并在在线跟踪过程中表现出稳定性问题和高模型复杂性.

研究的目的:

  • 为了解决当前对象跟踪方法的局限性,提出了一种新的追踪器,ASACTT.
  • 为了增强全球信息提取,提高目标特定的注意力,并确保强大的跟踪与适应性外观变化.

主要方法:

  • 改进了Swin-Transformer-Tiny骨干,用于增强全球信息提取.
  • 适应性稀疏注意力 (ASA) 机制,专注于搜索区域内的特定目标细节.
  • 动态模板更新器 (DTU) 使用位置编码和历史数据进行自适应的外观跟踪和降低复杂性.

主要成果:

  • 在五个基准数据集中,ASACTT与最先进的方法具有很高的可比性.
  • 在GOT-10K数据集上,在36FPS获得了75.3%的杰出成功得分.
  • 显著超越了具有可比模型参数的其他追踪器,表明精度和效率有所提高.
关键词:
适应性的稀疏注意力.对象追踪器可以追踪物体.西安人的网络网络.变压器变压器变压器

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结论:

  • 拟议的ASACTT追踪器有效地克服了全球信息捕获的局限性,并在基于变压器的语网络中准注意力.
  • ASACTT为对象跟踪提供了强大而高效的解决方案,平衡精度与减少模型复杂性.
  • 适应性稀疏注意力和动态模板更新器有助于在各种跟踪场景中实现卓越的性能和适应性.