视觉写作提示:以人物为基础的故事生成与策划图像序列

IF 4.2 1区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Transactions of the Association for Computational Linguistics Pub Date : 2023-01-20 DOI:10.1162/tacl_a_00553
Xudong Hong, A. Sayeed, K. Mehra, Vera Demberg, B. Schiele
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引用次数: 8

摘要

目前基于图像的故事生成工作的缺点是现有的图像序列集合背后没有连贯的情节。我们通过生成一个新的基于图像的数据集,视觉写作提示(VWP)来改进视觉故事生成。VWP包含近2K选定的电影镜头序列,每个序列包括5-10张图像。图像序列与通过众包收集的12K个故事相一致,这些故事提供了图像序列和相应图像序列中的一组基础人物。与以前的工作相比,我们新的图像序列收集和过滤过程使我们能够获得更加连贯,多样化和视觉接地的故事。我们还提出了一个基于角色的故事生成模型,该模型由连贯性驱动,作为一个强大的基线。评估表明,我们生成的故事比使用当前最先进的模型生成的故事更连贯、更有视觉基础、更多样化。我们的代码,图像功能,注释和收集的故事可以在https://vwprompt.github.io/上获得。
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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences
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/.
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来源期刊
CiteScore
32.60
自引率
4.60%
发文量
58
审稿时长
8 weeks
期刊介绍: The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.
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