A Semantic and Motion-Aware Spatiotemporal Transformer Network for Action Detection

This paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention model calculates the correlations between spatial and motion features to model spatiotemporal intera...

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Publié dans:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 9 vom: 14. Sept., Seite 6055-6069
Auteur principal: Korban, Matthew (Auteur)
Autres auteurs: Youngs, Peter, Acton, Scott T
Format: Article en ligne
Langue:English
Publié: 2024
Accès à la collection:IEEE transactions on pattern analysis and machine intelligence
Sujets:Journal Article
Description
Résumé:This paper presents a novel spatiotemporal transformer network that introduces several original components to detect actions in untrimmed videos. First, the multi-feature selective semantic attention model calculates the correlations between spatial and motion features to model spatiotemporal interactions between different action semantics properly. Second, the motion-aware network encodes the locations of action semantics in video frames utilizing the motion-aware 2D positional encoding algorithm. Such a motion-aware mechanism memorizes the dynamic spatiotemporal variations in action frames that current methods cannot exploit. Third, the sequence-based temporal attention model captures the heterogeneous temporal dependencies in action frames. In contrast to standard temporal attention used in natural language processing, primarily aimed at finding similarities between linguistic words, the proposed sequence-based temporal attention is designed to determine both the differences and similarities between video frames that jointly define the meaning of actions. The proposed approach outperforms the state-of-the-art solutions on four spatiotemporal action datasets: AVA 2.2, AVA 2.1, UCF101-24, and EPIC-Kitchens
Description:Date Revised 07.08.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1939-3539
DOI:10.1109/TPAMI.2024.3377192