  The canonical approach to video action recognition dictates a neural model to
do a classic and standard 1-of-N majority vote task. They are trained to
predict a fixed set of predefined categories, limiting their transferable
ability on new datasets with unseen concepts. In this paper, we provide a new
perspective on action recognition by attaching importance to the semantic
information of label texts rather than simply mapping them into numbers.
Specifically, we model this task as a video-text matching problem within a
multimodal learning framework, which strengthens the video representation with
more semantic language supervision and enables our model to do zero-shot action
recognition without any further labeled data or parameters requirements.
Moreover, to handle the deficiency of label texts and make use of tremendous
web data, we propose a new paradigm based on this multimodal learning framework
for action recognition, which we dub "pre-train, prompt and fine-tune". This
paradigm first learns powerful representations from pre-training on a large
amount of web image-text or video-text data. Then it makes the action
recognition task to act more like pre-training problems via prompt engineering.
Finally, it end-to-end fine-tunes on target datasets to obtain strong
performance. We give an instantiation of the new paradigm, ActionCLIP, which
not only has superior and flexible zero-shot/few-shot transfer ability but also
reaches a top performance on general action recognition task, achieving 83.8%
top-1 accuracy on Kinetics-400 with a ViT-B/16 as the backbone. Code is
available at https://github.com/sallymmx/ActionCLIP.git
