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Related Concept Videos

Fixed Action Patterns01:06

Fixed Action Patterns

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.
Force Classification01:22

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,...
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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 drone...
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Muscle Coordination and Action01:24

Muscle Coordination and Action

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.

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Related Experiment Video

Updated: Jun 18, 2026

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

XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition.

Kun-Yu Lin, Henghui Ding, Jia-Run Du

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 16, 2026
    PubMed
    Summary
    This summary is machine-generated.

    State-of-the-art open-vocabulary action recognition models fail to generalize across video domains. Our novel XOV-Action model enhances generalization to new action categories and unseen video domains.

    Related Experiment Videos

    Last Updated: Jun 18, 2026

    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

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Image-text foundation models show success, leading to video adaptation for open-vocabulary action recognition.
    • Current models struggle with generalization to diverse, unseen video domains and novel action categories.
    • Generalizable open-vocabulary action recognition aims to improve model robustness across domains and action sets.

    Purpose of the Study:

    • To develop a novel model, XOV-Action, for generalizable open-vocabulary action recognition.
    • To address challenges in understanding novel action concepts and mitigating domain discrepancies.
    • To introduce XOVABench, a new benchmark for evaluating cross-domain action recognition.

    Main Methods:

    • XOV-Action learns diversified elaboration representations for better open-set action generalization.
    • Scene-agnostic video representations are learned to overcome scene bias and improve domain generalization.
    • A new cross-domain action benchmark, XOVABench, is introduced for comprehensive evaluation.

    Main Results:

    • XOV-Action demonstrates improved performance on both closed-set and open-set action categories.
    • The model shows enhanced generalization capabilities across various video domains.
    • Experiments validate the effectiveness of XOV-Action in addressing domain shift and novel categories.

    Conclusions:

    • XOV-Action effectively improves generalizable open-vocabulary action recognition.
    • The proposed methods enhance model robustness to unseen domains and action categories.
    • XOVABench provides a valuable resource for future research in cross-domain action recognition.