Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2301.08571 v1 pith:7FOLZ27J submitted 2023-01-20 cs.CL cs.CVcs.LG

classification cs.CLcs.CVcs.LG
keywords imagegenerationsequencesstoriesstorycoherentsequencevisual
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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 and have more narrativity 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 have more narrativity than stories generated with the current state-of-the-art model.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Image Captioning to Visual Storytelling

    cs.CL 2025-07 unverdicted novelty 4.0 of 10

    Visual storytelling improves by treating it as image captioning followed by language-to-language story generation, with a new 'ideality' metric to gauge distance from an oracle.

  2. Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images

    cs.AI 2025-06 reject novelty 4.0 of 10

    SHE lowers behavioral hallucination scores by about 10 percent by detecting low visual-textual similarity and projecting out the hallucinated direction in embedding space.

Pith tools