2023
DOI: 10.48550/arxiv.2303.03166
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Faster Learning of Temporal Action Proposal via Sparse Multilevel Boundary Generator

Abstract: Temporal action localization in videos presents significant challenges in the field of computer vision. While the boundary-sensitive method has been widely adopted, its limitations include incomplete use of intermediate and global information, as well as an inefficient proposal feature generator. To address these challenges, we propose a novel framework, Sparse Multilevel Boundary Generator (SMBG), which enhances the boundary-sensitive method with boundary classification and action completeness regression. SMB… Show more

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