Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling

• URL: http://arxiv.org/abs/2008.04504v1
• Date: Tue, 11 Aug 2020 03:55:11 GMT
• Title: Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling
• Authors: Jiacheng Li, Siliang Tang, Juncheng Li, Jun Xiao, Fei Wu, Shiliang Pu, Yueting Zhuang
• Abstract summary: We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
• Score: 81.33107307509718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, which rely on a huge amount of human-annotated data. However, the annotation of VIST is extremely costly and many topics cannot be covered in the training dataset due to the long-tail topic distribution. In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting. Inspired by the way humans tell a story, we propose a topic adaptive storyteller to model the ability of inter-topic generalization. In practice, we apply the gradient-based meta-learning algorithm on multi-modal seq2seq models to endow the model the ability to adapt quickly from topic to topic. Besides, We further propose a prototype encoding structure to model the ability of intra-topic derivation. Specifically, we encode and restore the few training story text to serve as a reference to guide the generation at inference time. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model on BLEU and METEOR metric. The further case study shows that the stories generated after few-shot adaptation are more relative and expressive.

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