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Abnormal Action Detection Based on parameter-efficient transfer learning in Laboratory Scenarios

Computers, Materials & Continua 2024年第9期80卷 4219-4242页

作者： Changyu Liu Hao Huang Guogang Huang Chunyin Wu Yingqi Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhou510642China School of Oceanography Shanwei Institute of TechnologyShanwei516600China Technical Department Kerric(Guangdong)Laboratory Equipment Research and Manufacture Co.Ltd.Foshan528139China

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal *** enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training *** components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training *** TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant *** prefix training module facilitates efficient fine-tuning and knowledge *** results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR *** research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring *** proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

关键词： parameter-efficient transfer learning laboratory scenarios TubeRAPT abnormal action detection

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