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大连民族大学 机电工程学院,辽宁 大连 116600
Received:16 May 2022,
Revised:21 August 2022,
Published:25 December 2022
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毛琳,高航,杨大伟等.视频描述中链式语义生成网络[J].光学精密工程,2022,30(24):3198-3209.
MAO Lin,GAO Hang,YANG Dawei,et al.Chained semantic generation network for video captioning[J].Optics and Precision Engineering,2022,30(24):3198-3209.
毛琳,高航,杨大伟等.视频描述中链式语义生成网络[J].光学精密工程,2022,30(24):3198-3209. DOI: 10.37188/OPE.20223024.3198.
MAO Lin,GAO Hang,YANG Dawei,et al.Chained semantic generation network for video captioning[J].Optics and Precision Engineering,2022,30(24):3198-3209. DOI: 10.37188/OPE.20223024.3198.
针对视频描述中语义特征表达能力不足导致文本描述不准确问题,本文提出一种视频描述中链式语义生成网络(Chained Semantic generation Network,ChainS-Net)。构建了多阶段双路交叉的链式特征提取结构,该结构以全局域和局部域模块为基本单元,分别从视觉特征的全局和局部捕获视频语义;在网络的各阶段,将语义信息在全局域和局部域之间变换解析,实现视觉和语义信息的交互参考,提升语义特征表达能力;在此基础上,网络通过多阶段迭代的处理方式获取更为有效的语义表示,提升视频描述模型性能。在MSR-VTT和MSVD数据集上的实验结果表明,本文提出的链式语义生成网络ChainS-Net优于现有同类方法,相比于语义辅助视频描述网络(Semantics-Assisted Video Captioning network,SAVC),视频描述的四个评价指标平均提升了2.5%。
Aiming to address the unsatisfactory expression ability of semantics, which results in inaccurate text descriptions in video captioning, a chained semantic generation network (ChainS-Net) for video captioning is proposed. A multistage two-branch crossing chained feature extraction structure is constructed that uses global and local domain modules as basic units and captures the video semantics from global and local visual features, respectively. At each stage of the network, semantic information is transformed and parsed between the global and local domains. This method allows visual and semantic information to be cross referenced and improves the semantic expression ability. Furthermore, it allows a more effective semantic representation to be obtained through multistage iterative processing, thereby improving video captioning. Experimental results on MSR-VTT and MSVD datasets show that the proposed ChainS-Net outperforms other similar algorithms. Compared with the semantics-assisted video captioning network, SAVC, ChainS-Net shows average improvements of 2.5% in four metrics of video captioning.
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