  Research in the area of Vision and Language encompasses challenging topics
that seek to connect visual and textual information. The video-to-text problem
is one of these topics, in which the goal is to connect an input video with its
textual description. This connection can be mainly made by retrieving the most
significant 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
how the most reported benchmark datasets have been created, showing their
drawbacks and strengths for the problem requirements. We also show the
impressive progress that researchers have made on each dataset, and we analyze
why, despite this progress, the video-to-text conversion is still unsolved.
State-of-the-art techniques are still a long way from achieving human-like
performance in generating or retrieving video descriptions. We cover several
significant challenges in the field and discuss future research directions.
