Work

Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

Year: 2023

Type: article

Abstract: Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent, diverse, and visually grounded compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and diverse than stories generated with the current state-of-the-art model. Our code, image features, annotations and collected stories are available at https://vwprompt.github.io/.

Source: Transactions of the Association for Computational Linguistics

DOI: 10.1162/tacl_a_00553

Authors: Xudong Hong, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele

Institutions: Saarland University, University of Gothenburg

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: International Max Planck Research School for Environmental, Cellular and Molecular Microbiology, Vetenskapsrådet, Universität des Saarlandes, International Max Planck Research School for Advanced Methods in Process and Systems Engineering

Award: G2291942135