The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Deep learning from temporal coherence in video
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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“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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Metadata
- 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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