19 papers with code • 0 benchmarks • 7 datasets
The goal of automatic Video Description is to tell a story about events happening in a video. While early Video Description methods produced captions for short clips that were manually segmented to contain a single event of interest, more recently dense video captioning has been proposed to both segment distinct events in time and describe them in a series of coherent sentences. This problem is a generalization of dense image region captioning and has many practical applications, such as generating textual summaries for the visually impaired, or detecting and describing important events in surveillance footage.
These leaderboards are used to track progress in Video Description
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos.
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired.
The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips.
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.