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Description generation of open-domain videos incorporating multimodal features and bidirectional encoder

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Abstract

Describing open-domain videos in natural language is a major challenge for video understanding and can largely fulfill its potential in a host of applications, such as assisting blind people and managing massive videos. This paper presents an updated sequence-to-sequence video to text model (MM-BiS2VT), which incorporates multimodal feature fusion and bidirectional language structure and aims at optimizing conventional methods. The model totally considered four features-RGB images, optical flow, spatiotemporal and audio features. RGB images and optical flow features were extracted by ResNet152. And with the help of the improved three-dimensional convolutional neural networks model, spatiotemporal feature was included. As a vital factor to increase the accuracy of results, audio feature was also added to make up for visual information. After combining these features by a feature fusion method, bidirectional long short-term memory units (BiLSTMs) was adopted to generate descriptive sentences. The results indicate that fusing multimodal features could gain better sentences over other methods and BiLSTMs plays a significant role as well to improve the accuracy of the outputs, which means the works in this paper could be an available reference for computer vision and video processing.

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Acknowledgements

This work was supported by Research and Industrialization for Intelligent Video Processing Technology based on GPUs Parallel Computing of the Science and Technology Supported Program of Jiangsu Province (BY 2016003-11) and the Application platform and Industrialization for efficient cloud computing for Big data of the Science and Technology Supported Program of Jiangsu Province (BA2015052). We thank all the shared achievements of selfless antecessors.

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Correspondence to Xiaotong Du.

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Du, X., Yuan, J., Hu, L. et al. Description generation of open-domain videos incorporating multimodal features and bidirectional encoder. Vis Comput 35, 1703–1712 (2019). https://doi.org/10.1007/s00371-018-1591-x

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