Visual storytelling aims to automatically generate a coherent story based on
a given image sequence. Unlike tasks like image captioning, visual stories
should contain factual descriptions, worldviews, and human social commonsense
to put disjointed elements together to form a coherent and engaging
human-writeable story. However, most models mainly focus on applying factual
information and using taxonomic/lexical external knowledge when attempting to
create stories. This paper introduces SCO-VIST, a framework representing the
image sequence as a graph with objects and relations that includes human action
motivation and its social interaction commonsense knowledge. SCO-VIST then
takes this graph representing plot points and creates bridges between plot
points with semantic and occurrence-based edge weights. This weighted story
graph produces the storyline in a sequence of events using Floyd-Warshall's
algorithm. Our proposed framework produces stories superior across multiple
metrics in terms of visual grounding, coherence, diversity, and humanness, per
both automatic and human evaluations.

SCO-VIST 框架使用图表示形式，结合语义和基于发生的边权重，通过 Floyd-Warshall 算法生成视觉叙事。在多个指标上，该框架在视觉根植性、连贯性、多样性和人性上优于其他模型，经过自动和人工评估。