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      Beyond Short Snippets: Deep Networks for Video Classification

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          Abstract

          Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 72.8%).

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          LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

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            Author and article information

            Journal
            2015-03-31
            2015-04-13
            Article
            1503.08909
            51ef6ac2-41c1-4eb2-8917-43fdeac56f2c

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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            Custom metadata
            cs.CV

            Computer vision & Pattern recognition
            Computer vision & Pattern recognition

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