Cited by 691Speech and Audio ProcessingSpeech Recognition and SynthesisMultimedia Communication and Technology

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Out of Time: Automated Lip Sync in the Wild

Chung, Joon Son, Zisserman, Andrew·2017·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-54427-4_19

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Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Self-citation
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Untitled work
(1981) · Cited by 11,963
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Self-citation
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
Learning a Similarity Metric Discriminatively, with Application to Face Verification
Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335
Self-citation

Show all 30 references

Research works printed but not linked: 1

Geras, K.J., Mohamed, A.R., Caruana, R., Urban, G., Wang, S., Aslan, O., Philipose, M., Richardson, M., Sutton, C.: Compressing LSTMS into CNNS. arXiv preprint arXiv:1511.06433 (2015) · arXiv

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Sell, G., Duh, K., Snyder, D., Etter, D., & Garcia-Romero, D. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462122 · Cited by 34
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Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer
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Sun, Y., Zhou, H., Wang, K., Wu, Q., Hong, Z., Liu, J., Ding, E., Wang, J., Liu, Z., & Koike, H. (2022) · SIGGRAPH Asia Conference Papers · doi:10.1145/3550469.3555393 · Cited by 26
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Self-citation
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Self-citation
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Self-citation
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Authors: Chung, J. S., & Zisserman, A.
Affiliations: University of Oxford (GB)
Corresponding author: Chung, Joon Son
ORCID: Chung, Joon Son
Zisserman, Andrew
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2017
Pages: 251-263
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-54427-4_19
OpenAlex: W2604379605
Cited by: 689
References: 30
Citations by year: 2026: 79 · 2025: 102 · 2024: 135 · 2023: 96 · 2022: 76 · 2021: 80 · 2020: 51 · 2019: 31 · 2018: 27 · 2017: 10
FWCI: 23.11
Type: conference paper
Language: en
Open access: closed
Licence: www.springer.com/tdm (unspecified)
Locations: Lecture notes in computer science (publishedVersion)
Funders: Engineering and Physical Sciences Research Council (EP/M013774/1)
Topics: Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Engineering › Media Technology › Multimedia Communication and Technology
Keywords: lip synchronization, speech, two-stream convolutional neural network, unsupervised learning, active speaker detection, lip reading

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