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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
All About VLAD
TL;DR: It is shown that a simple change to the normalization method significantly improves retrieval performance and vocabulary adaptation can substantially alleviate problems caused when images are added to the dataset after initial vocabulary learning.
Proceedings ArticleDOI
Learning object categories from Google's image search
TL;DR: A new model, TSI-pLSA, is developed, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner, and can handle the high intra-class variability and large proportion of unrelated images returned by search engines.
Journal ArticleDOI
Efficient Additive Kernels via Explicit Feature Maps
Andrea Vedaldi,Andrew Zisserman +1 more
TL;DR: This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems.
Journal ArticleDOI
Scene Classification Using a Hybrid Generative/Discriminative Approach
TL;DR: This work introduces a novel vocabulary using dense color SIFT descriptors and investigates the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM).
Posted Content
Speeding up Convolutional Neural Networks with Low Rank Expansions
TL;DR: In this paper, the authors exploit cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain, which can be easily applied to existing CPU and GPU convolutional frameworks for tuneable speedup performance.