Video Storytelling: Textual Summaries for Events

TL;DR

This paper introduces the novel task of video storytelling, generating coherent stories for long videos by leveraging a context-aware neural framework and a reinforcement learning-based narrator, advancing multimedia understanding.

Contribution

It proposes a new framework combining a Residual Bidirectional RNN and a reinforcement learning narrator for effective video storytelling, addressing challenges of diversity and complexity.

Findings

Outperforms state-of-the-art baselines in quantitative metrics

Achieves higher user satisfaction in user studies

Introduces the Video Story dataset for future research

Abstract

Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this work, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a Residual Bidirectional Recurrent Neural Network to leverage contextual information from past and future. Second, we propose a Narrator model to discover the underlying storyline. The Narrator is formulated as a reinforcement learning agent which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the Video Story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines, and show that our method achieves better performance, in terms of quantitative measures and user study.