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相关概念视频

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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相关实验视频

Updated: May 11, 2026

Deep Neural Networks for Image-Based Dietary Assessment
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通过深度残留学习和选择性状态空间模型改进细粒食品的分类.

Chi-Sheng Chen1, Guan-Ying Chen2, Dong Zhou3

  • 1Neuro Industry, Inc., San Francisco, California, United States of America.

PloS one
|May 5, 2025
PubMed
概括

我们介绍ResVMamba,这是一种用于准确食品分类的新型模型,可以有效地捕捉全球和本地依赖. 这种计算营养方法为食品识别任务设定了新的基准.

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科学领域:

  • 计算营养学计算营养学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 食品分类对于计算营养和食品视觉任务至关重要.
  • 细粒度食品分类对传统的卷积神经网络 (CNN) 和视觉转换器 (ViT) 提出了挑战,原因是复杂性和计算成本.

研究的目的:

  • 开发一个高效和准确的食品分类模型.
  • 解决现有模型在处理复杂食品数据集方面的局限性.

主要方法:

  • 提出了ResVMamba模型,将残留学习策略集成到状态空间框架中.
  • 利用VMamba来有效地捕获全球和本地依赖关系.
  • 引入并使用CNFOOD-241食品数据集进行验证.

主要成果:

  • 在CNFOOD-241数据集上,ResVMamba获得了81.70%的Top-1精度和96.83%的Top-5精度.
  • 该模型在食品识别方面超过了当前最先进的 (SOTA) 模型.
  • 为CNFOOD-241数据集建立了一个新的绩效基准.

结论:

  • 在VMamba中开创了残留学习的集成,以实现增强的功能提取.
  • ResVMamba模型为食品分类提供了一个计算效率高,准确度高的解决方案.
  • 该研究介绍了CNFOOD-241数据集,并为ResVMamba模型提供了开源代码.