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

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

472
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
472
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

582
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
582

You might also read

Related Articles

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

Sort by
Same author

Locust density shapes energy metabolism and oxidative stress resulting in divergence of flight traits.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

The Association between Fast Food Outlets and Overweight in Adolescents Is Confounded by Neighbourhood Deprivation: A Longitudinal Analysis of the Millennium Cohort Study.

International journal of environmental research and public health·2021
Same author

24-Hour Physical Behavior Balance for Better Health for All: "The Sweet-Spot Hypothesis".

Sports medicine - open·2021
Same author

Is Olfactory Impairment Associated With 10-year Mortality Mediating by Neurodegenerative Diseases in Older Adults? The Four-Way Decomposition Analysis.

Frontiers in public health·2021
Same author

Higher one-year achievement rate of serum phosphate associated with lower cardiovascular mortality in hemodialysis patients.

BMC nephrology·2021
Same author

Prognostic validity of the American joint committee on cancer eighth edition staging system for well-differentiated pancreatic neuroendocrine tumors.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2021
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
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

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

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

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

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
See all related articles
  1. Home
  3. Effects Of Motion-relevant Knowledge From Unlabeled Video To Human-object Interaction Detection.
  1. Home
  3. Effects Of Motion-relevant Knowledge From Unlabeled Video To Human-object Interaction Detection.

Related Experiment Video

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K

Effects of Motion-Relevant Knowledge From Unlabeled Video to Human-Object Interaction Detection.

Xue Lin, Qi Zou, Xixia Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |December 10, 2021

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study leverages unlabeled videos to improve human-object interaction (HOI) detection, especially for rare categories. By learning motion patterns, the model enhances HOI detection accuracy with less labeled data.

    More Related Videos

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    6.8K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    58

    Related Experiment Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.2K
    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    6.8K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    58

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Human-object interaction (HOI) detection models struggle with insufficient labeled data, particularly for rare interaction categories.
    • Existing methods require large labeled datasets or rely heavily on pre-learned knowledge, limiting real-world applicability.

    Purpose of the Study:

    • To propose a multitask learning (MTL) framework that utilizes unlabeled videos to enhance HOI detection.
    • To address the challenge of limited labeled data for rare HOI categories by incorporating motion-relevant information.

    Main Methods:

    • A multitask learning (MTL) approach was developed, integrating self-supervised learning on unlabeled videos.
    • Key components include appearance reconstruction loss (ARL) and a sequential motion mining module to learn generalizable motion representations.
  • A domain discriminator was employed to bridge the domain gap between unlabeled videos and HOI images.

    • Main Results:

    • The proposed method demonstrated effectiveness in improving HOI detection, particularly for rare categories on the HICO-DET dataset.
    • Experiments on the V-COCO dataset showed strong performance under minimum supervision conditions.
    • The study confirmed the value of motion-aware knowledge from unlabeled videos for HOI detection.

    Conclusions:

    • Leveraging unlabeled videos through multitask learning and self-supervised motion representation learning is a viable strategy for enhancing HOI detection.
    • This approach effectively mitigates the limitations of scarce labeled data, especially for rare HOI categories.
    • The findings highlight the potential of utilizing readily available unlabeled video data in computer vision tasks.