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Updated: May 24, 2026

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

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TFRS: A task-level feature rectification and separation method for few-shot video action recognition.

Yanfei Qin¹, Baolin Liu¹

¹School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 30, 2024

Summary

This summary is machine-generated.

Few-shot video action recognition (FS-VAR) models struggle with biased support data. Our task-level feature rectification and separation (TFRS) method improves classification by leveraging prior knowledge to refine features and enhance separability.

Keywords:

Feature rectification Feature separation Few-shot video action recognition

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Few-shot video action recognition (FS-VAR) models require robust feature representation for unseen classes with limited data.
Existing FS-VAR methods are sensitive to support sample distribution, leading to biased classification due to insufficient representativeness and shared features.

Purpose of the Study:

To address the sample bias issue in FS-VAR.
To enhance the distinguishability and separability of features for improved classification accuracy.

Main Methods:

Propose a novel task-level feature rectification and separation (TFRS) method.
Leverage prior information from base classes to rectify support samples.
Remove commonality in task-level features to reduce bias.

Main Results:

TFRS significantly enhances performance across various established FS-VAR frameworks.
The method yields competitive results on benchmark datasets including UCF101, Kinetics, SSv2, and HMDB51.
Demonstrates improved feature distinguishability and separability in feature space.

Conclusions:

TFRS effectively resolves sample bias in FS-VAR.
The proposed method offers a versatile solution that can be integrated into existing FS-VAR frameworks.
Achieved state-of-the-art or competitive performance on multiple datasets, validating its efficacy.