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配对CNN-变压器功能用于人与物体交互检测.

Hutuo Quan1,2, Huicheng Lai1,2, Guxue Gao1,2

  • 1College of Computer Science and Technology, Xinjiang University, Urumqi 830017, China.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
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此摘要是机器生成的。

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本研究介绍了双向卷积神经网络 (CNN) - 变压器 (PCT) 用于人与物体交互 (HOI) 检测. PCT模型有效地结合了CNN和变压器的功能,在基准数据集上取得了竞争性结果.

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:

  • 人物对象交互 (HOI) 检测对于计算机理解场景语义至关重要.
  • 现有的两阶段HOI方法在功能质量方面表现出色,但缺乏上下文.
  • 基于一级变压器的方法捕捉了全球背景,但错过了对象检测的好处.

研究的目的:

  • 提出一种新的两阶段方法,将CNN和变压器方法的优势整合在一起,用于HOI检测.
  • 通过合并CNN和变压器功能来增强人物对对象的表现.
  • 改进HOI检测模型中的上下文理解.

主要方法:

  • 双向卷积神经网络 (CNN) - 变压器 (PCT) 模型被提出作为一个两阶段的方法.
  • 配对CNN功能是从CNN的骨干中提取出来的,并与配对变压器功能融合在一起.
  • 来自变压器的全球特征被用来提供上下文线索.

主要成果:

  • "CNN"和"变压器"特征的结合,使得对对对比的表现优于单个特征类型.
  • 实验性比较表明,CNN功能在HOI检测中保持着显著的优势.
  • 与HICO-DET和V-COCO数据集上最先进的方法相比,PCT模型实现了竞争性性能.
关键词:
计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.功能融合功能融合功能人类与物体相互作用检测检测变压器变压器变压器变压器

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结论:

  • 拟议的PCT模型有效地将对象检测功能与丰富的上下文信息相结合,用于HOI检测.
  • 整合CNN和变压器功能为HOI检测任务提供了协同优势.
  • 该研究强调了CNN功能在先进HOI检测架构中的持续相关性.