Visual Story Post-Editing
This work addresses the need for better evaluation and improvement of visual storytelling models, though it is incremental as it builds on existing datasets and models.
The authors tackled the problem of improving machine-generated visual stories by introducing the first dataset of human edits, VIST-Edit, containing 14,905 edited versions of 2,981 stories, and showed that leveraging these edits can boost model performance.
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dataset, VIST-Edit, includes 14,905 human edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions. We establish baselines for the task, showing how a relatively small set of human edits can be leveraged to boost the performance of large visual storytelling models. We also discuss the weak correlation between automatic evaluation scores and human ratings, motivating the need for new automatic metrics.