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Video description method based on high-order low-rank multi-modal attention mechanism

A video description, multi-modal technology, applied in the field of computer vision, can solve the problems of ignoring multi-modal feature correlation information, the impact of video description accuracy, etc., to achieve good application value, improve efficiency, and improve accuracy.

Active Publication Date: 2020-02-21
ZHEJIANG UNIV
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Problems solved by technology

The decoder generally uses a separate cyclic neural network combined with an attention mechanism, but the current attention mechanism ignores the correlation information between multi-modal features, which will affect the accuracy of video description

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  • Video description method based on high-order low-rank multi-modal attention mechanism

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Embodiment Construction

[0047]In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0048] On the contrary, the invention covers any alternatives, modifications, equivalent methods and schemes within the spirit and scope of the invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. The present invention can be fully understood by those skilled in the art without the description of these detailed parts.

[0049] refer to figure 1 , in a preferred embodiment of the present invention, the video description g...

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Abstract

The invention discloses a video description method based on a high-order low-rank multi-modal attention mechanism, which is used for generating short and accurate description for a given video clip. The method specifically comprises the following steps: obtaining a video data set for training a video description generation model, and defining an algorithm target; modeling time sequence multi-modalfeatures in the video data set; establishing a high-order low-rank multi-modal attention mechanism on a decoder based on the time sequence multi-modal characteristics; generating a description of aninput video using the model. The method is suitable for video description generation of a real video scene, and has better effect and robustness for various complex conditions.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a video description method based on a high-order low-rank multi-modal attention mechanism. Background technique [0002] In today's society, video has become an indispensable part of human society, it can be said that it is everywhere. Such an environment has made people's research on the semantic content of video has also been greatly developed. At present, most of the research on video is mainly concentrated on lower levels, such as classification, detection and so on. Thanks to the development of recurrent neural networks, the new task of video description generation has also come into view. Given a video clip, use the trained network model to automatically generate a sentence description for the video clip. Its application in the real world is also very extensive. For example, about 100 hours of videos are generated every minute on YouTube. If the generated video resources ar...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/41G06N3/045
Inventor 金涛李英明张仲非
Owner ZHEJIANG UNIV
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