PDEs on graphs for semi-supervised learning applied to first-person activity recognition in body-worn video-Reference-Cited by-同舟云学术

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PDEs on graphs for semi-supervised learning applied to first-person activity recognition in body-worn video

Published:2021 Issue:9 Volume:41 Page:4351
ISSN:1553-5231
Container-title:Discrete & Continuous Dynamical Systems
language:
Short-container-title:DCDS

Author:

Li Hao,Chen Honglin,Haberland Matt,Bertozzi Andrea L.,Brantingham P. Jeffrey

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Analysis

Reference48 articles.

1. G. Abebe and A. Cavallaro, A long short-term memory convolutional neural network for first-person vision activity recognition, in Proceedings of the IEEE International Conference on Computer Vision, 2017, 1339–1346.

2. K. Aizawa, K. Ishijima and M. Shiina, Summarizing wearable video, in Proceedings to 2001 International Conference on Image Processing, vol. 3, IEEE, 2001,398–401.

3. J. L. Barron, D. J. Fleet, S. S. Beauchemin.Performance of optical flow techniques, International Journal of Computer Vision, 12 (1994), 43-77.

4. A. L. Bertozzi, A. Flenner.Diffuse interface models on graphs for classification of high dimensional data, SIAM Review, 58 (2016), 293-328.

5. A. L. Bertozzi, X. Luo, A. M. Stuart, K. C. Zygalakis.Uncertainty quantification in graph-based classification of high dimensional data, SIAM/ASA Journal on Uncertainty Quantification, 6 (2018), 568-595.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Joint Reconstruction-Segmentation on Graphs;SIAM Journal on Imaging Sciences;2023-06-07

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