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Multi-Sentence Video Captioning using Content-oriented Beam Searching and Multi-stage Refining Algorithm
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DOI：10.1016/j.ipm.2020.102302.png)
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Abstract
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With the increasing growth of video data, especially in cyberspace, video captioning or the representation of video data in the form of natural language has been receiving an increasing amount of interest in several applications like video retrieval, action recognition, and video understanding, to name a few. In recent years, deep neural networks have been successfully applied for the task of video captioning. However, most existing methods describe a video clip using only one sentence that may not correctly cover the semantic content of the video clip. In this paper, a new multi-sentence video captioning algorithm is proposed using a content-oriented beam search approach and a multi-stage refining method. We use a new content-oriented beam search algorithm to update the probabilities of words generated by the trained deep networks. The proposed beam search algorithm leverages the high-level semantic information of an input video using an object detector and the structural dictionary of sentences. We also use a multi-stage refining approach to remove structurally wrong sentences as well as sentences that are less related to the semantic content of the video. To this intent, a new two-branch deep neural network is proposed to measure the relevance score between a sentence and a video. We evaluated the performance of the proposed method with two popular video captioning databases and compared the results with the results of some state-of-the-art approaches. The experiments showed the superior performance of the proposed algorithm. For instance, in the MSVD database, the proposed method shows an enhancement of 6% for the best-1 sentences in comparison to the best state-of-the-art alternative.
Keywords:
Multi-sentence video captioning
Beam search algorithm
Multimodal relevance measure network
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