Learning spatio-temporal representations with temporal squeeze pooling
This addresses video classification for computer vision applications, but appears incremental as it builds on existing CNNs.
The paper tackles video classification by proposing Temporal Squeeze pooling to extract movement information from video frames into squeezed images, achieving results compared to state-of-the-art on two benchmarks.
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.