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

Parallel Processing01:20

Parallel Processing

150
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
150
Schemas01:42

Schemas

11.6K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
11.6K

You might also read

Related Articles

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

Sort by
Same author

Nicotine Pouch Awareness, Susceptibility, and Use Among California College Students.

Journal of community health·2026
Same author

RNA editing for the treatment of alpha-1 antitrypsin deficiency.

Nucleic acids research·2026
Same author

Music festivals, exclusive concerts and reward programmes: nicotine pouch promotion on social media.

Tobacco control·2025
Same author

BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

The Prevention of Nicotine use in the State of Arkansas that has Geographic Tobacco use Disparities: the Perceived Prevention Needs and Realities of School Professionals.

Journal of community health·2025
Same author

Online Interest in Elf Bar in the United States: Google Health Trends Analysis.

Journal of medical Internet research·2024

Related Experiment Video

Updated: Jun 23, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.6K

HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group-Activity Scene Graph Generation in Videos.

Naga Venkata Sai Raviteja Chappa1, Pha Nguyen1, Thi Hoang Ngan Le1

  • 1Department of EECS, University of Arkansas, Fayetteville, AR 72701, USA.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

Predictive video scene understanding is advanced by a new Group-Activity Scene Graph Generation dataset and Hierarchical Attention-Flow (HAtt-Flow) mechanism. This flow-attention approach improves real-time relationship prediction in videos.

Keywords:
flow-attentiongroup activity recognitionvideo scene graph

More Related Videos

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.0K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K

Related Experiment Videos

Last Updated: Jun 23, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.6K
Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.0K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Group-Activity Scene Graph (GASG) generation is crucial for understanding dynamic video content.
  • Traditional methods for video scene graph generation (VidSGG) are limited to retrospective analysis, hindering predictive capabilities.

Purpose of the Study:

  • To introduce a novel dataset for GASG with detailed annotations.
  • To propose an innovative Hierarchical Attention-Flow (HAtt-Flow) mechanism for enhanced GASG performance.
  • To advance predictive video scene understanding.

Main Methods:

  • Developed a new GASG dataset by extending the JRDB dataset with appearance, interaction, position, relationship, and situation attributes.
  • Introduced the HAtt-Flow mechanism, applying flow network theory to attention mechanisms.
  • Transformed conventional attention 'values' and 'keys' into 'sources' and 'sinks' within the flow-attention framework.

Main Results:

  • The HAtt-Flow model demonstrated significant effectiveness in GASG tasks.
  • The proposed flow-attention mechanism showed superiority over existing methods.
  • Extensive experiments validated the model's performance and the novelty of the approach.

Conclusions:

  • The HAtt-Flow mechanism represents a significant advancement in predictive video scene understanding.
  • The developed dataset enriches scene understanding capabilities for complex activities.
  • This work provides valuable techniques for real-time relationship prediction in video data.