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Andrew Zisserman
Researcher at University of Oxford
Publications - 808
Citations - 312028
Andrew Zisserman is an academic researcher from University of Oxford. The author has contributed to research in topics: Convolutional neural network & Real image. The author has an hindex of 167, co-authored 808 publications receiving 261717 citations. Previous affiliations of Andrew Zisserman include University of Edinburgh & Microsoft.
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Proceedings ArticleDOI
Domain adaptation for upper body pose tracking in signed TV broadcasts
TL;DR: It is demonstrated that the transfer learning and person specific trackers significantly improve pose estimation performance, and a method for adapting existing training data to generate new training data by synthesis for signers with different appearances.
Journal ArticleDOI
AutoNovel: Automatically Discovering and Learning Novel Visual Categories.
TL;DR: In this paper, a self-supervised learning approach called AutoNovel is proposed to address the problem of discovering novel classes in an image collection given labelled examples of other classes.
Posted Content
My lips are concealed: Audio-visual speech enhancement through obstructions
TL;DR: A deep audio-visual speech enhancement network that is able to separate a speaker's voice by conditioning on both the speaker's lip movements and/or a representation of their voice by learning the representation on-the-fly given sufficient unobstructed visual input.
Journal ArticleDOI
Synthetic Humans for Action Recognition from Unseen Viewpoints
TL;DR: This work makes use of the recent advances in monocular 3D human body reconstruction from real action sequences to automatically render synthetic training videos for the action labels, and introduces a new data generation methodology that allows training of spatio-temporal CNNs for action classification.
Proceedings ArticleDOI
VHS to VRML: 3D graphical models from video sequences
TL;DR: A method to completely automatically recover 3D scene structure together with a camera for each frame from a sequence of images acquired by an unknown camera undergoing unknown movement is described.