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

Introduction to the Sign Test01:10

Introduction to the Sign Test

801
The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
801
Classification of Signals01:30

Classification of Signals

441
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...
441
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

124
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
124
Sign Test for Nominal Data01:12

Sign Test for Nominal Data

91
The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
For example, consider a...
91
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
Associative Learning01:27

Associative Learning

335
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...
335

You might also read

Related Articles

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

Sort by
Same author

[Scapular belt for the treatment of comminuted fractures of scapula].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2010
Same author

Manipulation of ordered nanostructures of protonated polyoxometalate through covalently bonded modification.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same author

Developments in nonsteroidal antiandrogens targeting the androgen receptor.

ChemMedChem·2010
Same author

Dynamic presentation of immobilized ligands regulated through biomolecular recognition.

Journal of the American Chemical Society·2010
Same author

[Research on crop-weed discrimination using a field imaging spectrometer].

Guang pu xue yu guang pu fen xi = Guang pu·2010
Same author

A palladium/copper bimetallic catalytic system: dramatic improvement for Suzuki-Miyaura-type direct C-H arylation of azoles with arylboronic acids.

Chemistry (Weinheim an der Bergstrasse, Germany)·2010
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2025

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
06:07

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

8.4K

Gloss Prior Guided Visual Feature Learning for Continuous Sign Language Recognition.

Leming Guo, Wanli Xue, Bo Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 30, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Gloss Prior Guidance Network (GPGN) to improve continuous sign language recognition (CSLR) by extracting generalizable visual features. The GPGN enhances CSLR models by leveraging gloss information as a prior, boosting performance on sign language benchmarks.

    More Related Videos

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    323
    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
    05:48

    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

    Published on: August 9, 2024

    1.5K

    Related Experiment Videos

    Last Updated: Jun 25, 2025

    Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
    06:07

    Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

    Published on: May 15, 2019

    8.4K
    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
    07:12

    Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

    Published on: April 11, 2025

    323
    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
    05:48

    Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

    Published on: August 9, 2024

    1.5K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Continuous Sign Language Recognition (CSLR) aims to interpret sign language from video data.
    • Improving the generalization of visual feature extractors in CSLR is crucial for real-world applications.
    • Existing methods often struggle with variations in signing styles and environmental conditions.

    Purpose of the Study:

    • To enhance the generalization ability of visual feature extractors for CSLR.
    • To introduce a novel Gloss Prior Guidance Network (GPGN) that utilizes gloss information as a prior.
    • To improve the accuracy and robustness of continuous sign language recognition systems.

    Main Methods:

    • A pre-trained gloss BERT model is used to extract signer-invariant gloss features.
    • A Gloss Prior Guidance Network (GPGN) with a Parallel Densely-connected Temporal Feature Extraction (PDC-TFE) module is proposed.
    • Cross-modality matching, formulated as a regularized optimal transport problem solved by the Sinkhorn algorithm, guides visual feature learning.
    • The GPGN is trained using a combination of cross-modality matching loss and Connectionist Temporal Classification (CTC) loss.

    Main Results:

    • The proposed GPGN achieves competitive performance on German and Chinese sign language recognition benchmarks.
    • Ablation studies confirm the effectiveness of key GPGN components, including the PDC-TFE module and cross-modality matching.
    • The pre-trained gloss BERT model and cross-modality matching approach demonstrate plug-and-play capabilities for enhancing existing CSLR methods.

    Conclusions:

    • The GPGN effectively improves the generalization of visual feature extractors in CSLR by incorporating gloss priors.
    • The proposed method offers a significant advancement in continuous sign language recognition accuracy and robustness.
    • The developed techniques can be readily integrated into other RGB-based CSLR systems to boost their performance.