  With the rapid development of deep learning algorithms, action recognition in
video has achieved many important research results. One issue in action
recognition, Zero-Shot Action Recognition (ZSAR), has recently attracted
considerable attention, which classify new categories without any positive
examples. Another difficulty in action recognition is that untrimmed data may
seriously affect model performance. We propose a composite two-stream framework
with a pre-trained model. Our proposed framework includes a classifier branch
and a composite feature branch. The graph network model is adopted in each of
the two branches, which effectively improves the feature extraction and
reasoning ability of the framework. In the composite feature branch, a
3-channel self-attention models are constructed to weight each frame in the
video and give more attention to the key frames. Each self-attention models
channel outputs a set of attention weights to focus on a particular aspect of
the video, and a set of attention weights corresponds to a one-dimensional
vector. The 3-channel self-attention models can evaluate key frames from
multiple aspects, and the output sets of attention weight vectors form an
attention matrix, which effectively enhances the attention of key frames with
strong correlation of action. This model can implement action recognition under
zero-shot conditions, and has good recognition performance for untrimmed video
data. Experimental results on relevant data sets confirm the validity of our
model.
