双流特征增强与融合的弱监督时序动作定位

打开文本图片集

关键词：弱监督；时序动作定位；空洞卷积；双流融合

中图分类号：TP391.4 文献标志码：A 文章编号：1001-3695（2025）07-039-2213-07

doi：10.19734/j. issn.1001-3695.2024.09.0373

Abstract：Weakly supervised temporalaction localizationaims to clasifyand locateaction instances inuntrimmed videos usingonlyvideo-levellabels.Existing models typicallusepre-trainedfeature extractors toextractsegment-levelRGBandopticalflowfeaturesfromvideos，butthepre-extractedsegment-levelvideofeaturesonlycovershorttimespansanddootonsider thecomplementarityandcorrelationbetweeRGBandopticalfo，hichaffctstheacuracyoflocalization.Totisnd，tis paper proposedaweakly-supervised temporalaction localization model withdual-streamfeatureenancementand fusion.Firstly，itexpandedthereceptivefieldthrough amulti-scaledensedilatedconvolution，alowing the modeltocover multipletime spansand capture the temporal dependenciesbetween video segments，resulting inenhancedRGBandopticalflow features. Then，itutilizedaconvolutionalnetworktoadaptivelyextractkeyfeaturesfromtheenhancedRGBandopticalflowfeaturesfor fusion，achievingcomplementarycorelationetweenRGBandopticalflowfeatures，furtherenrichingthevideofeaturerepre sentationand improving theaccuracyof themodel'slocalizationperformance.The modelachieves detectionaccuraciesof （20 73.9% and 43.5% on the THUMOS14 and ActivityNet1.3 datasets respectively，outperforming the existing state-of-the-art models，which proves the effectiveness of the proposed model.

Key Words：weakly supervision；temporal action localization；dilated convolution；dual-stream fusion

0 引言

时序动作定位作为视频理解领域中的关键任务，在诸如视频监控、异常检测和视频检索等实际应用中扮演着重要角色。（剩余19775字）