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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
122
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Aggregates Classification01:29

Aggregates Classification

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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...
391
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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相关实验视频

Updated: Sep 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

652

重新审视弱监督的哈希与深度多模式基础模型的哈希.

Min Wang, Wengang Zhou, Houqiang Li

    IEEE transactions on pattern analysis and machine intelligence
    |May 23, 2025
    PubMed
    概括

    本研究引入了一种新的弱监督哈希框架,用于使用视觉语言预训练 (VLP) 模型改进大规模图像检索. 该方法通过利用基于Web的标签来增强紧的图像表示,以获得更好的检索性能.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 视觉语言预训练 (VLP) 模型在多式联络任务中表现出色,但在大规模图像检索方面未得到充分探索.
    • 现实世界的图像检索系统使用网络抓取的图像与用户注释的标签,提供潜在的弱监督.
    • 现有的方法限制了对VLP模型的探索,以增强在检索中的紧图像表示.

    研究的目的:

    • 利用VLP基础模型的图像和文本对齐功能,以增强紧的图像表示.
    • 开发一个新的弱监督的散列框架,用于大规模的图像检索.
    • 通过利用用户注释标签的弱监督来提高图像检索系统的性能.

    主要方法:

    • 提出一个弱监督的哈希框架,它反复学习深度哈希网络,并增强弱监督.
    • 从VLP基础模型中提取图像和标签表示.
    • 采用政策梯度流程来优化检索性能 (mAP) 和概率决策流程来完善监督.

    主要成果:

    • 拟议的框架有效地增强了对大规模图像检索的紧图像表示.
    • 对公共数据集的实验表明,与现有方法相比,开发的方法的优越性.
    • 哈希网络的替代优化和弱监管导致检索指标的显著改进.

    更多相关视频

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

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    相关实验视频

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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

    Published on: December 15, 2023

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    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

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    结论:

    • 开发的弱监督的哈希框架成功地利用了VLP模型来改进图像检索.
    • 这种方法为在大型图像检索系统中利用网络取数据和VLP模型提供了一个有希望的方向.
    • 该方法提供了一个强大的和有效的解决方案,通过弱监督学习紧的图像表示.