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Cited by 2,684

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Convolutional Two-Stream Network Fusion for Video Action Recognition

Feichtenhofer, Christoph, Pinz, Axel, Zisserman, Andrew·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2016.213
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Abstract

Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters, (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy, finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results.

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Metadata

Authors
Feichtenhofer, C., Pinz, A., & Zisserman, A.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Pages
1933-1941
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2016.213
OpenAlex
W2342662179
Cited by
2,684
References
9
FWCI
100.55
Type
conference paper
Language
en
Open access
closed

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