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相关概念视频

Aggregates Classification01:29

Aggregates Classification

317
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...
317
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

112
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
112
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

318
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
318
Types of Aggregate Grading01:15

Types of Aggregate Grading

493
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
493
Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

106
Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
106

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相关实验视频

Updated: Jun 28, 2025

Profiling Maternal Behavior Responses During Whole-Brain Imaging
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Profiling Maternal Behavior Responses During Whole-Brain Imaging

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多颗粒度聚合与时空一致性用于基于视频的个人重新识别.

Hean Sung Lee1, Minjung Kim1, Sungjun Jang1

  • 1School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

时空多颗粒度聚合 (ST-MGA) 方法通过有效聚合时空特征来改进基于视频的个人重新识别 (ReID). 这种方法克服了诸如阻塞和检测错误等挑战,在基准数据集上取得了最先进的结果.

关键词:
注意力机制注意力机制互补学习是一种互补的学习.时间空间学习学习.基于视频的个人重新识别.

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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
  • 机器学习 机器学习

背景情况:

  • 基于视频的个人重新识别 (ReID) 依赖于空间和时间特征.
  • 现有的方法在与因遮蔽和检测错误引起的框架不一致性作斗争.
  • 这些不一致性阻碍了有效的时间处理和空间信息平衡.

研究的目的:

  • 提出一种新的方法,即时空间多颗粒度聚合 (ST-MGA),用于基于视频的强大人ReID.
  • 解决特征不一致问题,增强时空信息聚合.
  • 提高视频中人员重新识别的准确性和效率.

主要方法:

  • 开发了ST-MGA框架,包括提取,增强和聚合阶段.
  • 引入了一致的部分注意力 (CPA) 模块,用于时空对齐的部分提取.
  • 集成的多注意力部分增强 (MA-PA) 和长期/短期时间增强 (LS-TA) 块用于各种特征捕获.

主要成果:

  • 该CPA模块提取一致的和对齐的部分,减轻错位问题.
  • MA-PA和LS-TA块增强了空间和时间特征的多样性.
  • 通过分析部分关系和尺度,ST-MGA有效地汇总了多颗粒的时空模式.

结论:

  • 在MARS,DukeMTMC-VideoReID和LS-VID基准上,ST-MGA展示了最先进的性能.
  • 该方法成功地克服了视频ReID中阻塞和不完美的检测所带来的挑战.
  • 通过利用一致的时空线索,ST-MGA在基于视频的人重新识别方面取得了重大进展.