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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 Article
Segmenting Scenes by Matching Image Composites
TL;DR: This paper performs MRF-based segmentation that optimizes over matches, while respecting boundary information, and shows improved performance in detecting the principal occluding and contact boundaries for the scene over previous methods on data gathered from the LabelMe database.
Proceedings ArticleDOI
New approach to obtain height measurements from video
TL;DR: In this paper, a new measurement algorithm is presented which generates height measurements and their associated errors from a single known physical measurement in an image, which draws on results from projective geometry and computer vision.
Posted Content
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
TL;DR: Nearest-Neighbor Contrastive Learning of visual representations (NNCLR) as mentioned in this paper samples the nearest neighbors from the dataset in the latent space, and treats them as positives, which provides more semantic variations than pre-defined transformations.
Posted Content
VGGFace2: A dataset for recognising faces across pose and age.
TL;DR: VGGFace2 as mentioned in this paper is a large-scale face dataset with 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject.
Journal ArticleDOI
Automatic and Efficient Human Pose Estimation for Sign Language Videos
TL;DR: A fully automatic arm and hand tracker that detects joint positions over continuous sign language video sequences of more than an hour in length that outperforms the state-of-the-art long term tracker by Buehler et al. and achieves superior joint localisation results to those obtained using the pose estimation method of Yang and Ramanan.