Current methods for few-shot action recognition mainly fall into the metric
learning framework following ProtoNet. However, they either ignore the effect
of representative prototypes or fail to enhance the prototypes with multimodal
information adequately. In this work, we propose a novel Multimodal
Prototype-Enhanced Network (MORN) to use the semantic information of label
texts as multimodal information to enhance prototypes, including two modality
flows. A CLIP visual encoder is introduced in the visual flow, and visual
prototypes are computed by the Temporal-Relational CrossTransformer (TRX)
module. A frozen CLIP text encoder is introduced in the text flow, and a
semantic-enhanced module is used to enhance text features. After inflating,
text prototypes are obtained. The final multimodal prototypes are then computed
by a multimodal prototype-enhanced module. Besides, there exist no evaluation
metrics to evaluate the quality of prototypes. To the best of our knowledge, we
are the first to propose a prototype evaluation metric called Prototype
Similarity Difference (PRIDE), which is used to evaluate the performance of
prototypes in discriminating different categories. We conduct extensive
experiments on four popular datasets. MORN achieves state-of-the-art results on
HMDB51, UCF101, Kinetics and SSv2. MORN also performs well on PRIDE, and we
explore the correlation between PRIDE and accuracy