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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
On-the-fly specific person retrieval
TL;DR: A method of visual search for finding people in large video datasets that can be specified at run time by a text query, and a discriminative classifier for that person is then learnt on-the-fly using images downloaded from Google Image search.
Posted Content
The StreetLearn Environment and Dataset.
Piotr Mirowski,Andras Banki-Horvath,Keith Anderson,Denis Teplyashin,Karl Moritz Hermann,Mateusz Malinowski,Matthew Koichi Grimes,Karen Simonyan,Koray Kavukcuoglu,Andrew Zisserman,Raia Hadsell +10 more
TL;DR: To support and validate research in end-to-end navigation, StreetLearn is presented: an interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and performance baselines for a challenging goal-driven navigation task.
Proceedings Article
CrossTransformers: spatially-aware few-shot transfer
TL;DR: CrossTransformers as discussed by the authors employs self-supervised learning to encourage general-purpose features that transfer better, which can take a small number of labeled images and an unlabeled query, find coarse spatial correspondence between the query and the labeled images, and then infer class membership by computing distances between spatially-corresponding features.
Proceedings ArticleDOI
Who Are You? - Real-time Person Identification.
TL;DR: This paper presents a system for person identification that uses concise statistical models of facial features in a real-time realisation of the cast identification system of Everingham et al.
Proceedings ArticleDOI
Solving Markov Random Fields using Second Order Cone Programming Relaxations
TL;DR: A generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP) and proposing a second order cone programming relaxation scheme which solves a closely related (convex) approximation.