Learning Spatio-Temporal Representations with Temporal Squeeze Pooling

TY - GEN

T1 - Learning Spatio-Temporal Representations with Temporal Squeeze Pooling

AU - Huang, Guoxi

AU - Bors, Adrian Gheorghe

PY - 2020/5

Y1 - 2020/5

N2 - 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.

AB - 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.

U2 - 10.1109/ICASSP40776.2020.9054200

DO - 10.1109/ICASSP40776.2020.9054200

M3 - Conference contribution

BT - Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

PB - IEEE

ER -