Cited by 328Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsFace recognition and analysis

Deep learning from temporal coherence in video

Mobahi, Hossein, Collobert, Ronan, Weston, Jason·2009·Proceedings of the 26th Annual International Conference on Machine Learning

Abstract

This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks. 1.

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Registry

Crossref · 10.1145/1553374.1553469

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34 of 34 references verified0 citation errors · 0 not checkable

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  • This idea of continuity is used in a great number of artificial neural networks and related models.

    “This idea of continuity is used in a great number of artificial neural networks and related models (Wiskott and Sejnowski, 2002; Földiák, 2008; Mobahi et al., 2009).”

    From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Mobahi et al., 2009

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Authors
Mobahi, H., Collobert, R., & Weston, J.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • NEC Labs America, Princeton, NJ
Venue
Proceedings of the 26th Annual International Conference on Machine Learning
Publisher
ACM
Published
2009-06-14
Pages
737-744
OpenAlex
W2145038566
Cited by
328
References
35
Citations by year
2026: 3 · 2025: 4 · 2024: 7 · 2023: 13 · 2022: 18 · 2021: 31 · 2020: 35 · 2019: 28 · 2018: 33 · 2017: 30 · 2016: 42 · 2015: 32 · 2014: 11 · 2013: 11 · 2012: 13
FWCI
6.80
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Machine Learning (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
temporal coherence, unlabeled video, self-supervised learning, sequential data, face recognition

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