Decoupled header structure with attention mechanism.

<p>The decoupled header structure separates various tasks within the network into distinct components, allowing each component to specialize and optimize its operations. The integration of the attention mechanism further enhances the model’s ability to selectively focus on relevant features, i...

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Bibliographic Details
Main Author:	Baocheng Pei (20501647) (author)
Other Authors:	Yanan Sun (316649) (author), Yebiao Fu (20501650) (author), Ting Ren (1344075) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified spatial convolution operation phenomenon mainly lie feature extraction capability deformable attention mechanism achieve better results capture temporal information athletes &# 8217 individual target actions proposed method outperforms motion management scenarios temporal information motion management proposed method sports actions target overlap proposed methodology motion scene method include target detection xlink "> usually necessary two types tsm module sports scene sports level sports equipment specific moment source datasets network models module reduces main contributions first time experiments prove especially important designed network decoupling structure daily training conduct experiments classification tasks classification task analysis found accurately identify ablation experiments 5 score 298 %.
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Decoupled header structure with attention mechanism.

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