Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling
This addresses redundancy in visual storytelling for AI applications, but it is incremental as it builds on existing beam search techniques.
The paper tackles the problem of redundancy in visual storytelling by proposing an inter-sentence diverse beam search method to generate more expressive stories, resulting in reduced identical sentences for similar images compared to recent models.
Visual storytelling includes two important parts: coherence between the story and images as well as the story structure. For image to text neural network models, similar images in the sequence would provide close information for story generator to obtain almost identical sentence. However, repeatedly narrating same objects or events will undermine a good story structure. In this paper, we proposed an inter-sentence diverse beam search to generate a more expressive story. Comparing to some recent models of visual storytelling task, which generate story without considering the generated sentence of the previous picture, our proposed method can avoid generating identical sentence even given a sequence of similar pictures.