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Fixed Action Patterns

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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.

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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.
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Related Experiment Video

Updated: Nov 5, 2025

Corticospinal Excitability Modulation During Action Observation

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

Modeling Sub-Actions for Weakly Supervised Temporal Action Localization.

Linjiang Huang, Yan Huang, Wanli Ouyang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 13, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for weakly supervised temporal action localization by modeling sub-actions. This approach improves action detection by addressing the classification-detection contradiction, enhancing video understanding.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Weakly supervised temporal action localization is a challenging high-level video understanding task.
Current methods often face a contradiction between classification and detection due to reliance on video-level labels.

Purpose of the Study:

To alleviate the classification-detection contradiction in temporal action localization.
To improve the detection of complete action instances by explicitly modeling sub-actions.

Main Methods:

A novel framework utilizing learned prototypes to represent latent sub-actions.
Graph pooling to model inter-dependent sub-action relations and establish correspondences with actions.
Three complementary loss functions (representation, balance, and relation loss) for diverse and semantically meaningful sub-action learning.

Main Results:

Demonstrated effectiveness on THUMOS14 and ActivityNet1.3 datasets.
Achieved superior performance compared to state-of-the-art approaches in temporal action localization.

Conclusions:

Explicitly modeling sub-actions effectively addresses the classification-detection contradiction.
The proposed method enhances the accuracy and completeness of action instance detection in videos.