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

Fixed Action Patterns01:06

Fixed Action Patterns

18.2K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
18.2K
Muscle Coordination and Action01:24

Muscle Coordination and Action

3.9K
Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement....
3.9K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

715
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
715

You might also read

Related Articles

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

Sort by
Same author

Targeting m6A-SCG2-TAMs axis overcomes 5-FU resistance in colorectal cancer via a multi-omics model.

NPJ digital medicine·2026
Same author

Magnetic resonance imaging-guided thermoradiotherapy for glioblastoma using Gd<sup>3 +</sup>-loaded melanin nanoparticles reverses anoikis resistance through the ITGA5-PI3K/AKT axis.

Colloids and surfaces. B, Biointerfaces·2026
Same author

Piezo1-ATF3-PPP1r15a Axis Transduces Mechanical Stress into Apoptosis in Glioma Under Low-Intensity Focused Ultrasound.

Cancers·2026
Same author

New perspectives on fetal and neonatal alloimmune thrombocytopenia for obstetricians.

Frontiers in immunology·2026
Same author

Neuroimaging-driven recommendation systems for personalized sports training and injury prevention.

Scientific reports·2026
Same author

A novel dual elastography-based model for screening high-risk varices in hepatitis B virus-related cirrhosis.

Frontiers in medicine·2026

Related Experiment Video

Updated: Apr 13, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K

Brain-inspired multimodal motion and fine-grained action recognition.

Yuening Li1, Xiuhua Yang1, Changkui Chen2

  • 1Wuhan Sports University, Wuhan, China.

Frontiers in Neurorobotics
|February 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces FGM-CLIP, a novel multimodal approach for fine-grained action recognition. It significantly improves accuracy on complex videos by integrating visual and motion data.

Keywords:
CLIPaction recognitionbrain-inspiredclustering algorithmsmultimodal

More Related Videos

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

543
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

2.6K

Related Experiment Videos

Last Updated: Apr 13, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

543
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

2.6K

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Single-modality methods limit fine-grained action recognition, especially with complex actions and subtle motion variations.
  • Traditional approaches often use handcrafted features or simple CNNs, hindering effective multimodal fusion.

Purpose of the Study:

  • To enhance fine-grained action recognition using a novel multimodal architecture.
  • To overcome limitations of single-modality methods in complex video data.

Main Methods:

  • Introduced FGM-CLIP (Fine-Grained Motion CLIP), a novel architecture leveraging Contrastive Language-Image Pretraining (CLIP).
  • Integrated a fine-grained motion encoder and a multimodal fusion layer for end-to-end action recognition.
  • Jointly optimized visual and motion features to capture subtle action variations.

Main Results:

  • FGM-CLIP significantly outperformed existing methods on multiple fine-grained action recognition datasets.
  • The multimodal fusion strategy enhanced model robustness and accuracy for intricate action patterns.
  • Achieved higher classification accuracy in complex video data.

Conclusions:

  • FGM-CLIP offers a superior approach for fine-grained action recognition compared to traditional methods.
  • Multimodal fusion is crucial for accurately interpreting subtle actions and complex motion in videos.
  • The proposed architecture effectively leverages CLIP for enhanced video understanding.