基于多头集中注意力机制的无监督视频摘要模型

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中图分类号：TP391 文献标志码：A

Unsupervised Video Summriztion Model Bsed on Multi-hed Concentrtion Mechnism

LI Yujie ，b ， JIA Honn ， LING Lia ， ZHOU Wenkai°， JIANG Zhenga，DING Shuxue a，b ， TAN Benying a，b （a.SchoolofArtificial Intelligence，b.KeyLaboratoryofArtificial IntellgenceAlgorithmEngineeringof Guangxi Universities，Guilin Universityof Electronic Technology，Guilin 541OO4，Guangxi，China）

Abstract：Toadressthelimitations of existing video summarization methods inestablishing long-range frame dependenciesand paralelized training，anovel unsupervisedvideosummarizationmodel basedonthe multi-headcentralized atention mechanism（MH-CASUM）was proposed.The multi-head atention mechanism was integrated intothecentralized atentionmodel，thelengthregularizationlossfunction wasimproved，andthelossthreshold formodelparameterselection was optimized.The uniquenessand diversityof video frames were leveraged to enrich thesummary information，thereby the video summarization task was more eficiently accomplished.The performanceofthe MH-CASUM model was validated through evaluation experiments on SumMe and TVSum datasets using F1 score，Kendall correlation coefficient，and Spearmancorrelationcoeffcient.Theresultsshow thatthe introductionofmulti-headatentionmechanismandthe improved method for loss threshold inmodel parameter selection significantly enhance thevideo summarization performance of the MH-CASUM model. Compared to the previously best-performing unsupervised video summarization model CASUM，the （2号 F1 score of MH-CASUM on TVSum dataset is increased by 0.98% ，which proves its superiority and competitiveness in video summarization task.

Keywords： video summarization；attention mechanism；multi-head concentrated attention；unsupervised approach

随着互联网和信息技术的迅速发展，多媒体技术的广泛应用给人们的生活带了极大的便利，同时视频的“信息爆炸”也给人们带来诸多不便[1]。（剩余18759字）