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相关概念视频

Aggregates Classification01:29

Aggregates Classification

305
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Ogive Graph01:07

Ogive Graph

5.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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相关实验视频

Updated: Jun 10, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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多模态图形聚合变压器用于图像标题.

Lizhi Chen1, Kesen Li2

  • 1School of Computer Science and Technology, Soochow University, Suzhou 215000, China.

Neural networks : the official journal of the International Neural Network Society
|October 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了多模态图集成变压器 (MMGAT),用于改进图像标题. MMGAT模型有效地整合了多模式信息,大大提高了描述的准确性和在生成的标题中的上下文理解.

关键词:
图形聚合 图形聚合图片标题图片标题图片标题多模式的多模式变压器变压器变压器

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 当前的图像标题模型与上下文信息和对象关系作斗争.
  • 仅仅依赖区域特征就限制了对图像的语义理解.

研究的目的:

  • 开发一种新型模型,有效地整合多模式信息,以增强图像标题.
  • 解决现有方法在捕获上下文和语义细节方面的局限性.

主要方法:

  • 介绍了多模态图集成变压器 (MMGAT).
  • 以图形形式表示图像,其中有三个子图形:上下文网格,区域和语义文本.
  • 利用聚合器来引导消息在子图之间传递,以改进节点特征.

主要成果:

  • 在MS-COCO上获得了144.6%的CIDEr,在Flickr上获得了80.3%的CIDEr30k.
  • 在图像标题准确性和上下文理解方面显著改进.
  • 严格的分析证实了MMGAT设计的每个组件的有效性.

结论:

  • 该MMGAT模型成功地利用多模式图形聚合来实现卓越的图像标题.
  • 拟议的方法增强了模型理解和描述复杂图像内容的能力.
  • MMGAT代表了自动化图像描述领域的重大进步.