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Related Experiment Video

Updated: May 29, 2025

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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Pseudo label refining for semi-supervised temporal action localization.

Lingwen Meng¹, Guobang Ban¹, Guanghui Xi¹

¹Electric Power Research Institute of Guizhou Power Grid Co. Ltd, Guiyang, China.

|February 5, 2025

Summary

This study introduces Pseudo-Label Refining (PLR), a novel semi-supervised method for temporal action localization (TAL). PLR significantly enhances localization accuracy by refining pseudo-labels, outperforming existing methods with limited labeled data.

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Last Updated: May 29, 2025

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Training temporal action localization (TAL) models requires extensive manual annotation, which is time-consuming for videos.
Semi-supervised learning (SSL) offers a solution by leveraging both labeled and unlabeled data for model training.

Purpose of the Study:

To develop an effective semi-supervised temporal action localization (SSTAL) method to reduce reliance on fully annotated data.
To improve the accuracy and efficiency of pseudo-label generation and utilization in SSL for TAL.

Main Methods:

Proposed Pseudo-Label Refining (PLR) method using a teacher-student framework.
Incorporated pseudo-label self-refinement with temporal region interesting pooling for boundary accuracy.
Introduced a boundary synthesis module for refining temporal intervals via multiple inferences.
Implemented an adaptive weight learning strategy for progressive learning of pseudo-labels with varying quality.

Main Results:

Achieved significant improvements on THUMOS14 and ActivityNet v1.3 datasets using ActionFormer and BMN detectors.
Demonstrated superior localization accuracy compared to state-of-the-art SSTAL methods across 10%-60% labeling rates.
Ablation studies confirmed the effectiveness of individual modules within the PLR method.

Conclusions:

The PLR method effectively enhances the accuracy of pseudo-labels generated by teacher models.
PLR offers a robust solution for semi-supervised temporal action localization, significantly improving performance with reduced annotation effort.