Multi-view Distillation based on Multi-modal Fusion for Few-shot Action
  Recognition(CLIP-$\mathrm{M^2}$DF)
Fei Guo YiKang Wang Han Qi WenPing Jin Li Zhu
In recent years, few-shot action recognition has attracted increasing
attention. It generally adopts the paradigm of meta-learning. In this field,
overcoming the overlapping distribution of classes and outliers is still a
challenging problem based on limited samples. We believe the combination of
Multi-modal and Multi-view can improve this issue depending on information
complementarity. Therefore, we propose a method of Multi-view Distillation
based on Multi-modal Fusion. Firstly, a Probability Prompt Selector for the
query is constructed to generate probability prompt embedding based on the
comparison score between the prompt embeddings of the support and the visual
embedding of the query. Secondly, we establish a Multi-view. In each view, we
fuse the prompt embedding as consistent information with visual and the global
or local temporal context to overcome the overlapping distribution of classes
and outliers. Thirdly, we perform the distance fusion for the Multi-view and
the mutual distillation of matching ability from one to another, enabling the
model to be more robust to the distribution bias. Our code is available at the
URL: \url{https://github.com/cofly2014/MDMF}.