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Visual Storytelling

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arxiv 1604.03968 v1 pith:XC3NXIOK submitted 2016-04-13 cs.CL cs.AIcs.CV

Ting-Hao (Kenneth) Huang , Francis Ferraro , Nasrin Mostafazadeh , Ishan Misra , Aishwarya Agrawal , Jacob Devlin , Ross Girshick , Xiaodong He
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Pushmeet Kohli Dhruv Batra C. Lawrence Zitnick Devi Parikh Lucy Vanderwende Michel Galley Margaret Mitchell
This is my paper · ORCID
classification cs.CLcs.AIcs.CV
keywords storytellingdatasettaskvisualfirstlanguagealignedartificial
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We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND v.1, includes 81,743 unique photos in 20,211 sequences, aligned to both descriptive (caption) and story language. We establish several strong baselines for the storytelling task, and motivate an automatic metric to benchmark progress. Modelling concrete description as well as figurative and social language, as provided in this dataset and the storytelling task, has the potential to move artificial intelligence from basic understandings of typical visual scenes towards more and more human-like understanding of grounded event structure and subjective expression.

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Cited by 2 Pith papers

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

  1. LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Inserting a pixel-shuffle plus residual patch-merge layer inside the vision encoder compresses visual tokens more efficiently than post-encoder compression, at modest accuracy cost.

  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.

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