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Cited by 189

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Unsupervised Learning of Long-Term Motion Dynamics for Videos

Open access
Luo, Zelun, Peng, Boya, Huang, De-An, Alahi, Alexandre, Fei-Fei, Li·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.751
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Abstract

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, depth, and RGB-D videos.

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Show all 10 references

Research works printed but not linked: 43

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Metadata

Authors
Luo, Z., Peng, B., Huang, D.-A., Alahi, A., & Fei-Fei, L.
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2017-07
Pages
7101-7110
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2017.751
OpenAlex
W2578257192
Cited by
189
References
10
FWCI
8.90
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Unsupervised Learning of Long-Term Motion Dynamics for Videos. https://app.cochat.ai/papers/10.1109/cvpr.2017.751
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Luo, Z., Peng, B., Huang, D.-A., Alahi, A., & Fei-Fei, L. (2017). Unsupervised Learning of Long-Term Motion Dynamics for Videos. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.751

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