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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.
Papers
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Proceedings ArticleDOI
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes.
Olivia Wiles,Andrew Zisserman +1 more
TL;DR: A new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner is introduced that exceeds the state of the art on the ShapeNet benchmark dataset and is used to generate novel views of the sculpture dataset.
Posted Content
The AVA-Kinetics Localized Human Actions Video Dataset.
TL;DR: The AVA-Kinetics localized human actions video dataset is described, collected by annotating videos from the Kinetics-700 dataset using the AVA annotation protocol, and extending the original AVA dataset with these new AVA annotated Kinetics clips.
Posted Content
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
TL;DR: In this paper, an algorithm that can remove multiple sources of variation from the feature representation of a network was proposed to improve classification accuracies, when training networks on extremely biased datasets.
Book ChapterDOI
Vertebrae Detection and Labelling in Lumbar MR Images
TL;DR: It is shown that marrying two strong algorithms, the DPM object detector of Felzenszwalb et al. and inference using dynamic programming on chains, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance.
Posted Content
Deep Structured Output Learning for Unconstrained Text Recognition
Max Jaderberg,Max Jaderberg,Karen Simonyan,Karen Simonyan,Andrea Vedaldi,Andrew Zisserman,Andrew Zisserman +6 more
TL;DR: This paper proposed a convolutional neural network (CNN) based architecture which incorporates a Conditional Random Field (CRF) graphical model, taking the whole word image as a single input.