论文标题
Nits-VC Vatex视频字幕挑战2020
NITS-VC System for VATEX Video Captioning Challenge 2020
论文作者
论文摘要
视频字幕是将视频的内容，事件和动作汇总为简短的文本形式的过程，这些形式在许多研究领域都可以帮助您，例如视频指导的机器翻译，视频情感分析并为有需要的人提供援助。在本文中，提出了用于VATEX-2020视频字幕挑战的框架的系统描述。我们采用了基于编码器的方法，其中使用3D卷积神经网络（C3D）对视频的视觉特征进行编码，并且在解码阶段，使用了两个长期短期内存（LSTM）复发网络，在该网络中使用视觉特征和输入字幕分别融合，并通过在两个LSTMS之间执行元素的产品来生成最终输出。我们的模型能够在公共和私人测试数据集上分别达到0.20和0.22的BLEU得分。
Video captioning is process of summarising the content, event and action of the video into a short textual form which can be helpful in many research areas such as video guided machine translation, video sentiment analysis and providing aid to needy individual. In this paper, a system description of the framework used for VATEX-2020 video captioning challenge is presented. We employ an encoder-decoder based approach in which the visual features of the video are encoded using 3D convolutional neural network (C3D) and in the decoding phase two Long Short Term Memory (LSTM) recurrent networks are used in which visual features and input captions are fused separately and final output is generated by performing element-wise product between the output of both LSTMs. Our model is able to achieve BLEU scores of 0.20 and 0.22 on public and private test data sets respectively.
