融合语义增强与多注意力机制的视频描述方法.

With the explosive growth of video data, the task of video captioning had been paid more and more attention by researchers. How to enable computers to understand the content of the video and express it accurately was one of the difficult problems that had not been solved perfectly in the field of video captioning task. Aiming at the problems of insufficient use of semantic information and inaccurate description in the video captioning model, a video captioning method combining semantic enhancement and multi-attention mechanism was proposed based on the encoder-decoder framework in this paper. Firstly, visual text feature aggregation was used to provide high-level semantic guidance for model coding. Then, the Faster-RCNN network was used to extract the features of the video object, and the potential semantic information of the video object was obtained through the graph convolutional network, resulting in enhanced features. Finally, a multiple attention mechanism was introduced to better utilize input information and enhance the learning ability of the model. The experimental results on MSVD and MSR- VTT data sets showed that, compared with the benchmark model, the proposed method can reasonably optimize the input information of the video description model, effectively extract the video potential semantics, and solve the cross-modal problem of video text, and resolve the syntax structure of generated statements. This method can effectively improve the accuracy of video description model and the ability to describe complex scenes, showing its advancement. [ABSTRACT FROM AUTHOR]