Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Force Classification01:22

Force Classification

1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Functional Classification of Joints01:09

Functional Classification of Joints

3.6K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.6K
Aggregates Classification01:29

Aggregates Classification

289
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...
289
Associative Learning01:27

Associative Learning

239
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
239
Structural Classification of Joints01:20

Structural Classification of Joints

3.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.0K
Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

374
Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
374

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Prognostic Value of the SEA Score in Double-Negative DCP/AFP Unresectable HCC with Triple Therapy.

Journal of hepatocellular carcinoma·2026
Same author

SPP1 improves early risk assessment of postpartum adverse events in pulmonary arterial hypertension complicated with pregnancy.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

HiCAF-Net: A Hierarchical Cross-Attention Fusion framework for cross-cancer subtype classification using histopathological and genomic data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

The active role of pulmonary immunity in ischaemic stroke.

Journal of neuroinflammation·2026
Same author

A scalable multimodal framework for learning engagement recognition using three-dimensional convolutional neural networks and semi-automatic annotation.

Frontiers in artificial intelligence·2026
Same author

rhFGF21-loaded P hydrogel promoted diabetic wound repair by regulating the inflammatory cytokine expression and phagocytic function of macrophages via the STING-mediated autophagy pathway.

International immunopharmacology·2026

相关实验视频

Updated: May 10, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K

高级多样性特征学习用于行人属性识别.

Junyi Wu1, Yan Huang2, Min Gao3

  • 1Key Laboratory of Network Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China.

Neural networks : the official journal of the International Neural Network Society
|April 20, 2025
PubMed
概括

这项研究引入了一种新的高阶多样性特征学习 (HDFL) 方法,用于行人属性识别 (PAR). 该方法增强了细粒度特征提取,并整合了全球背景,在基准数据集上取得了最先进的结果.

关键词:
高层次的多样性是学习特征.步行者属性识别 步行者属性识别软冗余感知损失的感知损失

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.7K

相关实验视频

Last Updated: May 10, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.7K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 步行者属性识别 (PAR) 对于理解城市环境至关重要.
  • 现有的基于部分和基于注意力的 PAR 方法在细节捕捉和全球上下文集成方面存在局限性.
  • 当前的方法往往难以处理细微的细节,并且可能会受到不相关的信息的影响.

研究的目的:

  • 开发一种新的行人属性识别 (PAR) 方法,克服现有方法的局限性.
  • 通过整合高阶统计和全球背景来增强细粒度,属性特定特征的提取.
  • 为了提高PAR系统的准确性和稳定性.

主要方法:

  • 为 PAR 提出了一种高阶多样性特征学习 (HDFL) 方法,利用视觉转换器 (ViT).
  • 引入了特征特定的详细特征探索 (ADFE) 模块,使用多项式预测器捕获高阶统计数据和细粒度特征.
  • 开发了一种软冗余感知损失 (SPLoss),通过测量特征冗余来促进特征多样性.

主要成果:

  • 拟议的HDFL方法在多个PAR数据集上实现了最先进的 (SOTA) 性能.
  • 在具有挑战性的PA100K数据集上表现比以前的SOTA优于1.69%,达到84.92%的平均精度 (mA).
  • 证明了ADFE模块在生成详细,属性特定特征方面的有效性.

结论:

  • HDFL方法在行人属性识别方面取得了显著的进步.
  • 将详细关注与全球背景相结合,有效地解决了先前的 PAR 技术的局限性.
  • 拟议的方法为复杂的PAR任务提供了强大而准确的解决方案.