Second-order Temporal Pooling for Action Recognition
نویسندگان
چکیده
منابع مشابه
Second-order Temporal Pooling for Action Recognition
Most successful deep learning models for action recognition generate predictions for short video clips, which are later aggregated into a longer time-frame action descriptor by computing a statistic over these predictions. Zeroth (max) or first order (average) statistic are commonly used. In this paper, we explore the benefits of using second-order statistics. Specifically, we propose a novel e...
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Encouraged by the success of Convolutional Neural Networks (CNNs) in image classification, recently much effort is spent on applying CNNs to video based action recognition problems. One challenge is that video contains a varying number of frames which is incompatible to the standard input format of CNNs. Existing methods handle this issue either by directly sampling a fixed number of frames or ...
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Most video based action recognition approaches create the video-level representation by temporally pooling the features extracted at each frame. The pooling methods that they adopt, however, usually completely or partially neglect the dynamic information contained in the temporal domain, which may undermine the discriminative power of the resulting video representation since the video sequence ...
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ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2018
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-018-1111-5