REVnet: Bring Reviewing Into Video Captioning for a Better Description
Huidong Li, Dandan Song, Lejian Liao, Cuimei Peng · 2019
Recently, the task of automatically generating a textual description of a video is attracting increasing interest. The attention-based encoder-decoder framework has been extensively applied in this domain. However, compared with other captioning tasks, such as image captioning, video captioning is more challenging because semantic information among frames is hard to be extracted. In this paper, we propose a reviewing network (REVnet) to reconstruct the previous hidden state, which is combined with the conventional encoder-decoder framework. REVnet brings backward flow into the caption generation process, which encourages the hidden state embedding more information and enables the semantics of the generated sentence more coherent. Furthermore, REVnet can regularize the attention mechanism within the framework, which encourages the model better utilizing the semantic information extracted from multiple different frames. Our experimental results on benchmark datasets demonstrate that our proposed REVnet has a significant improvement over the baseline method. Furthermore, we use a reinforcement learning method to finetune the model, and get better results than the state-of-the-art methods.