Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parallel Processing

Parallel Processing

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...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Force Classification

Force Classification

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,...

Convolution Properties II

Convolution Properties II

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...

Sign Test for Matched Pairs

Sign Test for Matched Pairs

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...

Cross Product

Cross Product

The cross product is a fundamental concept in vector algebra that is a vector operation on two different vectors to obtain a third vector. Unlike the scalar product, the cross product results in a vector quantity perpendicular to both the original vectors.
The magnitude of the cross product is obtained by multiplying the magnitude of both the vectors and the sine of the angle between them. This means that a larger angle between the vectors will lead to a greater magnitude of the cross product.

You might also read

Related Articles

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

Sort by

Same author

Retraction Note: Targeted production of reactive oxygen species in mitochondria to overcome cancer drug resistance.

Nature communications·2026

Same author

Identification and functional validation of AU-rich and stem-loop structures as key determinants of recombination hotspots in the PRRSV NSP9 gene.

The Journal of general virology·2026

Same author

Physical activity determinants, 24-h movement behaviors, and cardiovascular fitness in young adults: an integrated theoretical model perspective.

Frontiers in psychology·2026

Same author

Relationship Between Sleep and Meal Timing with Glycemia Parameters in Individuals with Obesity Participating in a Randomized Time-Restricted Eating Study.

Nutrients·2026

Same author

Feasibility of remotely delivered Tai Chi on older adults' 24-hour movement behaviors: A crossover randomized controlled trial.

Complementary therapies in clinical practice·2026

Same author

The 3'Untranslated region is a critical determinant of Getah virus replication, pathogenesis, and vector competence.

Virulence·2026

Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026

Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026

Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026

Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 26, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Pairwise Two-Stream ConvNets for Cross-Domain Action Recognition With Small Data.

Zan Gao, Leming Guo, Tongwei Ren

IEEE Transactions on Neural Networks and Learning Systems

|December 9, 2020

Summary

This summary is machine-generated.

This study introduces a novel Pairwise Two-stream ConvNets (PTC) algorithm for cross-domain action recognition (CDAR) with limited labeled data. The PTC model effectively leverages source domain data to improve target domain recognition accuracy and efficiency.

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Nov 26, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Cross-domain action recognition (CDAR) faces challenges with limited labeled target domain data.
Existing methods struggle with domain shift in video action recognition.

Purpose of the Study:

To propose a novel end-to-end Pairwise Two-stream ConvNets (PTC) algorithm for CDAR under limited sample conditions.
To develop a method that effectively leverages source domain data for improved target domain performance.

Main Methods:

Employs a pairwise network architecture to utilize source domain samples.
Integrates frame self-attention and adaptive weighting for RGB and flow feature fusion.
Introduces a sphere boundary sample-selecting scheme to enhance generalization.

Main Results:

PTC outperforms state-of-the-art methods in accuracy and training efficiency on newly constructed CDAR datasets (SDAI Action I & II).
Achieves significant accuracy improvements (e.g., 21.9% over two-stream models) with minimal target domain samples.
Demonstrates effective learning of domain-invariant features.

Conclusions:

The proposed PTC algorithm is highly effective for CDAR with limited labeled data.
The novel methods enhance generalization and accuracy in challenging real-life conditions.
The released SDAI Action datasets will foster future research in CDAR.