  The Contrastive Language-Image Pre-training (CLIP) has recently shown
remarkable generalization on "zero-shot" training and has applied to many
downstream tasks. We explore the adaptation of CLIP to achieve a more efficient
and generalized action recognition method. We propose that the key lies in
explicitly modeling the motion cues flowing in video frames. To that end, we
design a two-stream motion modeling block to capture motion and spatial
information at the same time. And then, the obtained motion cues are utilized
to drive a dynamic prompts learner to generate motion-aware prompts, which
contain much semantic information concerning human actions. In addition, we
propose a multimodal communication block to achieve a collaborative learning
and further improve the performance. We conduct extensive experiments on
HMDB-51, UCF-101, and Kinetics-400 datasets. Our method outperforms most
existing state-of-the-art methods by a significant margin on "few-shot" and
"zero-shot" training. We also achieve competitive performance on "closed-set"
training with extremely few trainable parameters and additional computational
costs.
