Semantic Enhanced Encoder-Decoder Network (SEN) for Video Captioning

Yuling Gui, Dan Guo, Ye Zhao
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引用次数: 2

Abstract

Video captioning is a challenging problem in neural networks, computer vision, and natural language processing. It aims to translate a given video into a sequence of words which can be understood by humans. The dynamic information in videos and the complexity in linguistic cause the difficulty of this task. This paper proposes a semantic enhanced encoder-decoder network to tackle this problem. To explore a more abundant variety of video information, it implements a three path fusion strategy in the encoder side which combines complementary features. In the decoding stage, the model adopts an attention mechanism to consider the different contributions of the fused features. In both the encoder and decoder side, the video information is well obtained. Furthermore, we use the idea of reinforcement learning to calculate rewards based on semantic designed computation. Experimental results on Microsoft Video Description Corpus (MSVD) dataset show the effectiveness of the proposed approach.
语义增强的视频字幕编解码器网络(SEN)
视频字幕是神经网络、计算机视觉和自然语言处理中的一个具有挑战性的问题。它旨在将给定的视频翻译成人类可以理解的单词序列。视频信息的动态性和语言的复杂性导致了该任务的难度。本文提出了一种语义增强的编码器-解码器网络来解决这个问题。为了挖掘出更加丰富多样的视频信息,该算法在编码器端实现了互补特征相结合的三路径融合策略。在解码阶段,该模型采用注意机制来考虑融合特征的不同贡献。在编码器和解码器两方面,都能很好地获取视频信息。此外,我们使用强化学习的思想来计算基于语义设计计算的奖励。在微软视频描述语料库(MSVD)数据集上的实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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