A Pipeline for Creative Visual Storytelling

Abstract

Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and crossdisciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of taskmodules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation.

Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision

“…Moving from objects to actions, several tasks have been proposed to mimic more realistic settings where a higher degree of integration between modalities is required. One is visual storytelling (Huang et al, 2016;Gonzalez-Rico and Pineda, 2018;Lukin et al, 2018), where models have to understand the action depicted in each photo and their relations to generate a story. Similar abilities are required in the task of generating non-grounded, human-like questions about an image (Mostafazadeh et al, 2016;Jain et al, 2017), and in that of asking discriminative questions over pairs of similar scenes .…”

The Steep Road to Happily Ever after: an Analysis of Current Visual Storytelling Models

“…First, all were publicly available and well-documented, ensuring easy replicability. Other existing visual storytelling models (Huang et al, 2016;Yu et al, 2017;Hsu et al, 2018;Lukin et al, 2018) would have required reimplementation. Doing so introduces the possibility of unintentionally crippling performance (e.g., when setting required but unreported parameters), which we wished to avoid.…”

Proceedings of the Second Workshop on Shortcomings in Vision and Language

“…First, all were publicly available and well-documented, ensuring easy replicability. Other existing visual storytelling models Yu et al, 2017;Hsu et al, 2018;Lukin et al, 2018) would have required reimplementation. Doing so introduces the possibility of unintentionally crippling performance (e.g., when setting required but unreported parameters), which we wished to avoid.…”