Huang, Guoxi and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2020) Learning Spatio-Temporal Representations with Temporal Squeeze Pooling. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Mar 2020 16:40 |
Last Modified: | 18 Dec 2024 00:38 |
Published Version: | https://doi.org/10.1109/ICASSP40776.2020.9054200 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICASSP40776.2020.9054200 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158187 |