Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

586
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
586
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Convolution Properties I01:20

Convolution Properties I

595
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
595
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

978
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
978

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Runx1-Snx9 axis drives the pathological secretion of mitochondrial-derived vesicles to activate cGAS-STING signaling in acute pancreatitis.

Journal of nanobiotechnology·2026
Same author

Ultra-sensitive humidity sensors based on a MoS<sub>2</sub>/graphene Schottky diode.

Nanoscale·2026
Same author

MZB1 Modulates Inflammatory Severity in Severe Acute Pancreatitis through an IgA-Associated Intestinal Barrier Axis.

Inflammation·2026
Same author

Breaking the oncogene-immune suppression cycle through dual HER2 silencing and innate immune activation by biomineralized DNA nanocomplexes.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

Extracellular vesicles derived from hADSCs rescue acute pancreatitis by carrying <i>p</i>-STK3 to regulate Treg differentiation.

iScience·2026
Same author

Humidity sensing characteristics of graphene and MoS<sub>2</sub> as well as their heterostructures with different stacking configurations.

Nanoscale·2026

Related Experiment Video

Updated: Feb 1, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

874

P_VggNet: A convolutional neural network (CNN) with pixel-based attention map.

Kunhua Liu1, Peisi Zhong1, Yi Zheng1

  • 1Advanced Manufacturing Technology Center, Shandong University of Science and Technology, Qingdao, Shandong province, China.

Plos One
|December 13, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces P-VggNet, a novel convolutional neural network (CNN) structure designed for enhanced image feature extraction across all image types. Experiments show P-VggNet outperforms VggNet-16 in efficiency.

More Related Videos

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.4K

Related Experiment Videos

Last Updated: Feb 1, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

874
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.4K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Attention maps integrated into VggNet (EAC-Net) improved performance but were limited to facial action unit detection.
  • Existing methods were specialized and not broadly applicable to diverse image types.

Purpose of the Study:

  • To propose a new convolutional neural network (CNN) structure, P-VggNet, for general image feature extraction.
  • To enhance the utility of attention maps beyond facial action unit detection.

Main Methods:

  • A novel P-Net was designed and integrated with a 16-layer VggNet (VggNet-16) to create the P-VggNet architecture.
  • The P-VggNet structure was developed for broader application in image analysis.

Main Results:

  • P-VggNet demonstrated superior efficiency in extracting image features compared to VggNet-16.
  • Two experiments confirmed the effectiveness of the proposed P-VggNet structure.

Conclusions:

  • The proposed P-VggNet structure offers a more efficient approach to image feature extraction.
  • P-VggNet advances the application of attention maps in CNNs for general image analysis tasks.