  Zero-shot action recognition, which recognizes actions in videos without
having received any training examples, is gaining wide attention considering it
can save labor costs and training time. Nevertheless, the performance of
zero-shot learning is still unsatisfactory, which limits its practical
application. To solve this problem, this study proposes a framework to improve
zero-shot action recognition using human instructions with text descriptions.
The proposed framework manually describes video contents, which incurs some
labor costs; in many situations, the labor costs are worth it. We manually
annotate text features for each action, which can be a word, phrase, or
sentence. Then by computing the matching degrees between the video and all text
features, we can predict the class of the video. Furthermore, the proposed
model can also be combined with other models to improve its accuracy. In
addition, our model can be continuously optimized to improve the accuracy by
repeating human instructions. The results with UCF101 and HMDB51 showed that
our model achieved the best accuracy and improved the accuracies of other
models.
