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Higher-order pooling of cnn features via kernel linearization for action recognition

Cherian, Anoop; Koniusz, Piotr; Gould, Stephen

Description

ost successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
URI: http://hdl.handle.net/1885/218090
Source: Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
DOI: 10.1109/WACV.2017.22

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