Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling

TL;DR

This paper introduces a novel end-to-end model for visual storytelling that leverages external knowledge and group-wise semantics to generate more coherent and imaginative stories from images.

Contribution

It proposes a knowledge-enriched attention network combined with a group-wise semantic module within a unified encoder-decoder framework for improved storytelling.

Findings

Outperforms state-of-the-art methods on Visual Storytelling dataset

Enhances story coherence with implicit external knowledge integration

Improves narrative quality through group-wise semantic modeling

Abstract

As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent story. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the popular Visual Storytelling dataset with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods.