Solving the visual symbol grounding problem has long been a goal of
artificial intelligence. The field appears to be advancing closer to this goal
with recent breakthroughs in deep learning for natural language grounding in
static images. In this paper, we propose to translate videos directly to
sentences using a unified deep neural network with both convolutional and
recurrent structure. Described video datasets are scarce, and most existing
methods have been applied to toy domains with a small vocabulary of possible
words. By transferring knowledge from 1.2M+ images with category labels and
100,000+ images with captions, our method is able to create sentence
descriptions of open-domain videos with large vocabularies. We compare our
approach with recent work using language generation metrics, subject, verb, and
object prediction accuracy, and a human evaluation.

本篇论文提出直接利用统一深度神经网络将视频转换为句子的方法，并通过将知识从含标记种类超过 120 万张图像和带字幕的超过 100,000 张图像中迁移而创建具有大型词汇库的开放域视频句子描述。通过与语言生成度量，主语，动词和宾语预测准确度以及人的评估进行比较，证明本方法的有效性。