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Transferable Unintentional Action Localization With Language-Guided Intention Translation.

Jinglin Xu, Yongming Rao, Jie Zhou

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This study introduces a new framework for unintentional action localization (UAL) in videos. It uses language-guided intention translation to improve detection of unintentional actions, outperforming existing methods in open-set scenarios.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Unintentional action localization (UAL) is difficult due to the need to interpret subtle intention clues.
Existing methods often treat UAL as binary classification, limiting performance in open-set scenarios.

Purpose of the Study:

To develop a novel framework for unintentional action localization (UAL) that explicitly addresses open-set scenarios.
To improve the understanding and detection of unintentional actions in videos by leveraging language-guided intention translation.

Main Methods:

Proposed a Transferable Unintentional Action Localization framework utilizing language-guided intention translation.
Employed a transformer architecture for knowledge transfer between intentional and unintentional action segments.
Introduced a dense voting scheme for detecting action transitions using discriminative representations.

Main Results:

The proposed framework achieved superior performance compared to existing UAL methods across various open-set scenarios.
Demonstrated enhanced generalization ability by extending the framework to competitive sports and a new dataset (FS-Falls).

Conclusions:

Language-guided intention translation offers a powerful approach for UAL, improving performance in open-set conditions.
The framework provides a new perspective for creating representations with complete action intention priors for better human action understanding.