  Zero-Shot Action Recognition has attracted attention in the last years and
many approaches have been proposed for recognition of objects, events and
actions in images and videos. There is a demand for methods that can classify
instances from classes that are not present in the training of models,
especially in the complex problem of automatic video understanding, since
collecting, annotating and labeling videos are difficult and laborious tasks.
We have identified that there are many methods available in the literature,
however, it is difficult to categorize which techniques can be considered state
of the art. Despite the existence of some surveys about zero-shot action
recognition in still images and experimental protocol, there is no work focused
on videos. Therefore, we present a survey of the methods that comprise
techniques to perform visual feature extraction and semantic feature extraction
as well to learn the mapping between these features considering specifically
zero-shot action recognition in videos. We also provide a complete description
of datasets, experiments and protocols, presenting open issues and directions
for future work, essential for the development of the computer vision research
field.
