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相关概念视频

Transformers01:26

Transformers

1.0K
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...
1.0K
Types Of Transformers01:16

Types Of Transformers

941
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...
941
Encoding01:19

Encoding

109
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
109
Source Transformation01:15

Source Transformation

3.2K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
3.2K
Energy Losses in Transformers01:21

Energy Losses in Transformers

818
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
818
Stereotype Content Model02:16

Stereotype Content Model

13.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.9K

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相关实验视频

Updated: May 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

340

多层次的语义意识转换器用于图像标题.

Qin Xu1, Shan Song2, Qihang Wu2

  • 1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, 230601, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, 230601, China; School of Computer Science and Technology, Anhui University, Hefei, 230601, China.

Neural networks : the official journal of the International Neural Network Society
|March 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于图像标题的多级语义意识转换器 (MLSAT). 通过专注于突出物体,MLSAT增强了视觉表现,大大提高了标题的性能,与现有的方法相比.

关键词:
注意力机制注意力机制图片标题图片标题图片标题相对空间关系的相对空间关系.变压器变压器变压器

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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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:

  • 有效的视觉表现是图像标题的关键.
  • 当前基于网格的方法经常错过突出对象的细粒度语义细节.

研究的目的:

  • 提出一种新的图像标题方法,即多层次语义意识转换器 (MLSAT).
  • 通过整合上下文细节和专注于突出对象的高级语义信息来改善视觉表示.

主要方法:

  • 开发了视觉内容引导注意力 (VCGA) 来建模空间相关性和语义相互作用.
  • 引入了多级语义意识 (MLSA) 模块,用于精细的语义信息提取和集成.
  • 在MLSA模块中使用了语义信息提取器 (SIE),语义精炼器 (SR) 和视觉语义融合块 (V-SFB).

主要成果:

  • 拟议的MLSAT方法在MS-COCO数据集上显著优于最先进的模型.
  • 在官方在线测试服务器上获得了135.1%的CIDEr (c40) 评分.
  • 在捕获突出对象信息以增强图像标题方面表现出卓越的性能.

结论:

  • MLSAT有效地解决了现有方法中碎片化视觉特征的局限性.
  • 多层次语义信息的整合导致更准确,更符合背景的图像字幕.
  • 拟议的方法代表了图像标题领域的重大进步.