Contextualize, Show and Tell: A Neural Visual Storyteller

Abstract

We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015). This extension relies on an encoder LSTM to compute a context vector of each story from the image sequence. This context vector is used as the first state of multiple independent decoder LSTMs, each of which generates the portion of the story corresponding to each image in the sequence by taking the image embedding as the first input. Our model showed competitive results with the METEOR metric and human ratings in the internal track of the Visual Storytelling Challenge 2018.

References in corpus (2)

• Efficient Estimation of Word Representations in Vector Space
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Cited by in corpus (6)

• "My Way of Telling a Story": Persona based Grounded Story Generation
• Induction and Reference of Entities in a Visual Story
• Visual Storytelling via Predicting Anchor Word Embeddings in the Stories
• Incorporating Textual Evidence in Visual Storytelling
• Character-Centric Storytelling
• Plot and Rework: Modeling Storylines for Visual Storytelling