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This report describes our submission to the ActivityNet Challenge at CVPR 2019.
Ensemble methods in machine learning
T. G. Dietterich · 2000
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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ADAM: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Audio-visual speech recognition using deep learning
K. Noda, Y. Yamaguchi, K. Nakadai, H. G. Okuno, and T. Ogata · 2015
Earlier work this paper cites.
Lipnet: End-to-end sentence-level lipreading
Y. M. Assael, B. Shillingford, S. Whiteson, and N. De Freitas · 2016
Earlier work this paper cites.
Cross-modal supervision for learning active speaker detection in video
P. Chakravarty and T. Tuytelaars · 2016
Cited alongside, same era.
Out of time: automated lip sync in the wild
J. S. Chung and A. Zisserman · 2016
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
The conversation: Deep audio-visual speech enhancement
T. Afouras, J. S. Chung, and A. Zisserman · 2018
Cited alongside, same era.
Looking to listen at the cocktail party: a speaker-independent audio-visual model for speech separation
A. Ephrat, I. Mosseri, O. Lang, T. Dekel, K. Wilson, A. Hassidim, W. T. Freeman, and M. Rubinstein · 2018
Later among the works it cites.
Deep audio-visual speech recognition
T. Afouras, J. S. Chung, A. Senior, O. Vinyals, and A. Zisserman · 2019
Closest in time.
Perfect match: Improved cross-modal embeddings for audio-visual synchronisation
S.-W. Chung, J. S. Chung, and H.-G. Kang · 2019
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AVA-ActiveSpeaker: An audio-visual dataset for active speaker detection
J. Roth, S. Chaudhuri, O. Klejch, R. Marvin, A. Gallagher, L. Kaver, S. Ramaswamy, A. Stopczynski, C. Schmid, Z. Xi, et al · 2019
Closest in time.
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