2024/08/12 by Yingjin Song, Song, Yingjin, Denis Paperno +3
Computer Science · Health Professions · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Digital Storytelling and Education #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2408.06259
openalex publication_date 2024/08/12 · openalex created_date 2024/09/10 · openalex updated_date 2026/07/28
Visual storytelling systems generate multi-sentence stories from image sequences. In this task, capturing contextual information and bridging visual variation bring additional challenges. We propose a simple yet effective framework that leverages the generalization capabilities of pretrained foundation models, only training a lightweight vision-language mapping network to connect modalities, while incorporating context to enhance coherence. We introduce a multimodal contrastive objective that also improves visual relevance and story informativeness. Extensive experimental results, across both automatic metrics and human evaluations, demonstrate that the stories generated by our framework are diverse, coherent, informative, and interesting.