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

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Deep learning from temporal coherence in video

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

DOI 10.1145/1553374.1553469

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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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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
DOI: 10.1145/1553374.1553469
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
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
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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Deep learning from temporal coherence in video

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