  The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically
related to the nature of semantic side information used to transfer knowledge,
although this aspect has not been primarily investigated in the literature.
This work introduces a new ZSAR method based on the relationships of
actions-objects and actions-descriptive sentences. We demonstrate that
representing all object classes using descriptive sentences generates an
accurate object-action affinity estimation when a paraphrase estimation method
is used as an embedder. We also show how to estimate probabilities over the set
of action classes based only on a set of sentences without hard human labeling.
In our method, the probabilities from these two global classifiers (i.e., which
use features computed over the entire video) are combined, producing an
efficient transfer knowledge model for action classification. Our results are
state-of-the-art in the Kinetics-400 dataset and are competitive on UCF-101
under the ZSAR evaluation. Our code is available at
https://github.com/valterlej/objsentzsar
