Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research

TL;DR

This paper introduces a large-scale video description dataset derived from Descriptive Video Service (DVS), enabling improved research in video annotation with minimal manual effort.

Contribution

The authors present an automated method to extract and align DVS annotations, creating the largest DVS-based video description dataset to date.

Findings

Over 84.6 hours of paired video and sentences collected

Automated DVS segmentation and alignment method developed

Dataset is scalable and minimally reliant on human intervention

Abstract

In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video/sentences from 92 DVDs and is growing.