  In this paper, we propose a new video representation learning method, named
Temporal Squeeze (TS) pooling, which can extract the essential movement
information from a long sequence of video frames and map it into a set of few
images, named Squeezed Images. By embedding the Temporal Squeeze pooling as a
layer into off-the-shelf Convolution Neural Networks (CNN), we design a new
video classification model, named Temporal Squeeze Network (TeSNet). The
resulting Squeezed Images contain the essential movement information from the
video frames, corresponding to the optimization of the video classification
task. We evaluate our architecture on two video classification benchmarks, and
the results achieved are compared to the state-of-the-art.
