Abstract
Image narrative generation describes the creation of stories regarding the content of image data from a subjective viewpoint. Given the importance of the subjective feelings of writers, characters, and readers in storytelling, image narrative generation methods must consider human emotion, which is their major difference from descriptive caption generation tasks. The development of automated methods to generate story-like text associated with images may be considered to be of considerable social significance, because stories serve essential functions both as entertainment and also for many practical purposes such as education and advertising. In this study, we propose a model called ViNTER (Visual Narrative Transformer with Emotion arc Representation) to generate image narratives that focus on time series representing varying emotions as "emotion arcs," to take advantage of recent advances in multimodal Transformer-based pre-trained models. We present experimental results of both manual and automatic evaluations, which demonstrate the effectiveness of the proposed emotion-aware approach to image narrative generation.
Abstract (translated)
URL
https://arxiv.org/abs/2202.07305
