  The goal of spatial-temporal action detection is to determine the time and
place where each person's action occurs in a video and classify the
corresponding action category. Most of the existing methods adopt
fully-supervised learning, which requires a large amount of training data,
making it very difficult to achieve zero-shot learning. In this paper, we
propose to utilize a pre-trained visual-language model to extract the
representative image and text features, and model the relationship between
these features through different interaction modules to obtain the interaction
feature. In addition, we use this feature to prompt each label to obtain more
appropriate text features. Finally, we calculate the similarity between the
interaction feature and the text feature for each label to determine the action
category. Our experiments on J-HMDB and UCF101-24 datasets demonstrate that the
proposed interaction module and prompting make the visual-language features
better aligned, thus achieving excellent accuracy for zero-shot spatio-temporal
action detection. The code will be available at
https://github.com/webber2933/iCLIP.
