  Research in the Vision and Language area encompasses challenging topics that
seek to connect visual and textual information. When the visual information is
related to videos, this takes us into Video-Text Research, which includes
several challenging tasks such as video question answering, video summarization
with natural language, and video-to-text and text-to-video conversion. This
paper reviews the video-to-text problem, in which the goal is to associate an
input video with its textual description. This association can be mainly made
by retrieving the most relevant descriptions from a corpus or generating a new
one given a context video. These two ways represent essential tasks for
Computer Vision and Natural Language Processing communities, called text
retrieval from video task and video captioning/description task. These two
tasks are substantially more complex than predicting or retrieving a single
sentence from an image. The spatiotemporal information present in videos
introduces diversity and complexity regarding the visual content and the
structure of associated language descriptions. This review categorizes and
describes the state-of-the-art techniques for the video-to-text problem. It
covers the main video-to-text methods and the ways to evaluate their
performance. We analyze twenty-six benchmark datasets, showing their drawbacks
and strengths for the problem requirements. We also show the progress that
researchers have made on each dataset, we cover the challenges in the field,
and we discuss future research directions.
