Stories for Images-in-Sequence by using Visual and Narrative Components

TL;DR

This paper introduces a novel sequence-to-sequence model with dual encoders for generating coherent, human-like stories from sequences of images, capturing both visual and narrative context for improved storytelling.

Contribution

It proposes a new encoder architecture with separate encoders for image sequences and previous stories, enhancing story flow and narrative quality in image sequence storytelling.

Findings

Generated stories are more human-like and coherent.

Manual evaluation confirms improved story quality.

Model captures temporal dependencies effectively.

Abstract

Recent research in AI is focusing towards generating narrative stories about visual scenes. It has the potential to achieve more human-like understanding than just basic description generation of images- in-sequence. In this work, we propose a solution for generating stories for images-in-sequence that is based on the Sequence to Sequence model. As a novelty, our encoder model is composed of two separate encoders, one that models the behaviour of the image sequence and other that models the sentence-story generated for the previous image in the sequence of images. By using the image sequence encoder we capture the temporal dependencies between the image sequence and the sentence-story and by using the previous sentence-story encoder we achieve a better story flow. Our solution generates long human-like stories that not only describe the visual context of the image sequence but also contains narrative and evaluative language. The obtained results were confirmed by manual human evaluation.