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VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube 7,000 + speakers VoxCeleb contains speech from speakers spanning a wide range of different ethnicities, accents, professions and ages. Utterance Lengths 1 million + utterances All speaking face-tracks are captured "in the wild", with background chatter, laughter, overl
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Arpit Mittal, Andrew Zisserman
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Qiong Cao, Li Shen, Weidi Xie,
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Abhishek Dutta, Ankush Gupta an
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Maria-Elena Nilsback and Andrew
VGG Convolutional Neural Networks Practical By Andrea Vedaldi and Andrew Zisserman This is an Oxford Visual Geometry Group computer vision practical, authored by Andrea Vedaldi and Andrew Zisserman (Release 2017a). Convolutional neural networks are an important class of learnable representations applicable, among others, to numerous computer vision problems. Deep CNNs, in particular, are composed
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Omkar M. Parkhi, Andrea Vedaldi
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Karen Simonyan and Andrew Zisse
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Omkar M Parkhi and Andrea Vedal
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Carlos Arteta, Victor Lempitsky
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies Click on a dataset category to
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies James Philbin, Relja Arandjelov
This website uses Google Analytics to help us improve the website content. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here Accept cookies
Please report any bugs to Andrew Zisserman [email] The complete set of these functions are available as a gzipped tar file allfns.tar.gz, or as a zip file allfns.zip. NOTE, it is recommended that the complete set is downloaded as many of the functions use other functions. MIT License Acknowledgements: These functions are written by: David Capel, Andrew Fitzgibbon, Peter Kovesi, Tomas Werner, Yoni
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