  While video action recognition has been an active area of research for
several years, zero-shot action recognition has only recently started gaining
traction. In this work, we propose a novel end-to-end trained transformer model
which is capable of capturing long range spatiotemporal dependencies
efficiently, contrary to existing approaches which use 3D-CNNs. Moreover, to
address a common ambiguity in the existing works about classes that can be
considered as previously unseen, we propose a new experimentation setup that
satisfies the zero-shot learning premise for action recognition by avoiding
overlap between the training and testing classes. The proposed approach
significantly outperforms the state of the arts in zero-shot action recognition
in terms of the the top-1 accuracy on UCF-101, HMDB-51 and ActivityNet
datasets. The code and proposed experimentation setup are available in GitHub:
https://github.com/Secure-and-Intelligent-Systems-Lab/SemanticVideoTransformer
