17 papers with code • 0 benchmarks • 6 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.
Automatic evaluation of text generation tasks (e. g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE.
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
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.
This paper strives to find amidst a set of sentences the one best describing the content of a given image or video.
Scene-aware dialog systems will be able to have conversations with users about the objects and events around them.
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.
Video captioning is an advanced multi-modal task which aims to describe a video clip using a natural language sentence.