Changyu Liu1, Hao Huang1, Guogang Huang2,*, Chunyin Wu1, Yingqi Liang3
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4219-4242, 2024, DOI:10.32604/cmc.2024.053625
Abstract Laboratory safety is a critical area of broad societal concern, particularly in the detection of abnormal actions. To enhance the efficiency and accuracy of detecting such actions, this paper introduces a novel method called TubeRAPT (Tubelet Transformer based on Adapter and Prefix Training Module). This method primarily comprises three key components: the TubeR network, an adaptive clustering attention mechanism, and a prefix training module. These components work in synergy to address the challenge of knowledge preservation in models pre-trained on large datasets while maintaining training efficiency. The TubeR network serves as the backbone for spatio-temporal… More >
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