Abstract
Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.
Abstract (translated)
视频字幕是自动生成视频中动作的文本描述的任务。尽管之前的工作(例如序列到序列模型)已经在抽象描述短视频的粗略描述方面显示出有希望的结果,但对包含多个细粒度操作的视频添加详细描述仍然非常具有挑战性。本文旨在通过提出一种用于视频字幕的新型分层强化学习框架来解决这一挑战,其中高级管理器模块学习设计子目标,而低级别工作者模块识别基本动作以实现子目标。利用这种组合框架来加强不同级别的视频字幕,我们的方法明显优于新引入的用于细粒度视频字幕的大规模数据集的所有基线方法。此外,我们的非集成模型已经在广泛使用的MSR-VTT数据集上取得了最新的成果。
URL
https://arxiv.org/abs/1711.11135