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

Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
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...
3.2K
Classification of Systems-II01:31

Classification of Systems-II

137
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
137
Aggregates Classification01:29

Aggregates Classification

306
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...
306
Classification of Systems-I01:26

Classification of Systems-I

177
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
177
Classification of Signals01:30

Classification of Signals

420
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
420

You might also read

Related Articles

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

Sort by
Same author

Target, dose and spacing in network-guided intermittent theta burst stimulation for Parkinson's disease.

Parkinsonism & related disorders·2026
Same author

Deep Learning Algorithm Based on Contrast-Enhanced Ultrasound Potentially Optimizes Treatment Strategies for Solitary Primary Hepatocellular Carcinoma.

Ultrasound in medicine & biology·2026
Same author

Outcomes of an optimized ciclosporin-free haploidentical HSCT protocol in paediatric patients with cerebral adrenoleukodystrophy.

British journal of haematology·2026
Same author

CRISPR/Cas12a-powered tri-mode aptasensor for ultrasensitive and multiplexed detection of microcystin-LR.

Biosensors & bioelectronics·2026
Same author

The Correlation Between Apathy and the Efficacy of Rehabilitation in Patients With Parkinson's Disease: A Retrospective Observational Study.

Brain and behavior·2026
Same author

Can the Use of Telehealth Guidance Services Reduce Depressive Symptoms Among Family Caregivers of Older Adults with Cognitive Impairment? A Moderated-Mediation Model.

Healthcare (Basel, Switzerland)·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2025

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

Coarse-Fine Nested Network for Weakly Supervised Group Activity Recognition.

Xiaojing Ge, Rui Yan, Xiangbo Shu

    IEEE Transactions on Neural Networks and Learning Systems
    |September 16, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel coarse-fine nested network (CFNN) for weakly supervised group activity recognition (WSGAR). The CFNN effectively identifies group behaviors by localizing key visual patches and learning both local and global features, outperforming existing methods.

    More Related Videos

    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

    487
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.0K

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    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
    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

    487
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.0K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Weakly supervised group activity recognition (WSGAR) traditionally relies on person detection, limiting flexibility.
    • Existing methods struggle with redundant and ambiguous information from treating all local visual data equally.

    Purpose of the Study:

    • To develop a novel network, the coarse-fine nested network (CFNN), for improved WSGAR.
    • To overcome limitations of traditional person-detection-dependent methods and noisy grid features.

    Main Methods:

    • Proposes a coarse-fine nested network (CFNN) that avoids explicit person detection.
    • Introduces a nested interactor (NI) for modeling spatiotemporal interactions.
    • Employs a coarse-grained spatial localizer (CSL) and a fine-grained spatiotemporal selector (FSS) for feature extraction.

    Main Results:

    • The CFNN effectively localizes key visual patches relevant to group activities.
    • The network learns both local and global features for robust activity recognition.
    • Experiments on Volleyball and NBA datasets show superior performance compared to existing methods.

    Conclusions:

    • The proposed CFNN demonstrates significant effectiveness in weakly supervised group activity recognition.
    • The coarse-to-fine approach successfully addresses challenges in identifying group behaviors without fine-grained supervision.
    • The method offers a more flexible and accurate solution for WSGAR tasks.