H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 175 Citations 301,948 596 World Ranking 4 National Ranking 1

Research.com Recognitions

Awards & Achievements

2008 - Distinguished Fellow of the British Machine Vision Association (BMVA)

2007 - Fellow of the Royal Society, United Kingdom

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Andrew Zisserman mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Contextual image classification and Machine learning. His work is connected to Object detection, Image, Object, Feature extraction and Cognitive neuroscience of visual object recognition, as a part of Artificial intelligence. Andrew Zisserman combines topics linked to Detector with his work on Computer vision.

His studies deal with areas such as Class, Classifier, Bag-of-words model and Visual Word as well as Contextual image classification. His study on Machine learning also encompasses disciplines like

  • Optical flow, State and Motion most often made with reference to Training set,
  • Word recognition that intertwine with fields like Convolutional neural network. The concepts of his Convolutional neural network study are interwoven with issues in Face, Spotting and Scale.

His most cited work include:

  • Very Deep Convolutional Networks for Large-Scale Image Recognition (32308 citations)
  • Multiple view geometry in computer vision (14643 citations)
  • Very Deep Convolutional Networks for Large-Scale Image Recognition (13064 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Image and Machine learning. His Artificial intelligence study frequently draws connections between related disciplines such as Natural language processing. His Computer vision study incorporates themes from Invariant and Affine transformation.

His research combines Contextual image classification and Pattern recognition. His work in Image is not limited to one particular discipline; it also encompasses Set. His Object study focuses on Object detection in particular.

He most often published in these fields:

  • Artificial intelligence (75.12%)
  • Computer vision (41.38%)
  • Pattern recognition (27.79%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (75.12%)
  • Pattern recognition (27.79%)
  • Computer vision (41.38%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Speech recognition. Andrew Zisserman interconnects Task and Natural language processing in the investigation of issues within Artificial intelligence. Andrew Zisserman does research in Pattern recognition, focusing on Convolutional neural network specifically.

His Convolutional neural network research is multidisciplinary, relying on both Pipeline and Deep learning. Particularly relevant to Segmentation is his body of work in Computer vision. His study looks at the relationship between Speech recognition and fields such as Benchmark, as well as how they intersect with chemical problems.

Between 2017 and 2021, his most popular works were:

  • VGGFace2: A Dataset for Recognising Faces across Pose and Age (966 citations)
  • VoxCeleb2: Deep Speaker Recognition. (596 citations)
  • Objects that Sound (213 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Andrew Zisserman spends much of his time researching Artificial intelligence, Face, Speech recognition, Pattern recognition and Computer vision. Andrew Zisserman has included themes like Machine learning and Set in his Artificial intelligence study. His research in Face tackles topics such as Pose which are related to areas like Monocular, Gesture and Sequence.

His Speech recognition research includes elements of Audio visual, Word, Task, Benchmark and Speech enhancement. The various areas that Andrew Zisserman examines in his Pattern recognition study include Contextual image classification, Embedding and Coordinate system. His study brings together the fields of Modality and Computer vision.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Very Deep Convolutional Networks for Large-Scale Image Recognition

Karen Simonyan;Andrew Zisserman.
computer vision and pattern recognition (2014)

42954 Citations

Multiple view geometry in computer vision

Richard Hartley;Andrew Zisserman.
(2000)

27767 Citations

The Pascal Visual Object Classes (VOC) Challenge

Mark Everingham;Luc Gool;Christopher K. Williams;John Winn.
International Journal of Computer Vision (2010)

9180 Citations

Multiple View Geometry in Computer Vision (2nd ed)

Richard Hartley;Andrew Zisserman.
(2003)

4151 Citations

A Comparison of Affine Region Detectors

K. Mikolajczyk;T. Tuytelaars;C. Schmid;A. Zisserman.
International Journal of Computer Vision (2005)

3982 Citations

Deep Face Recognition.

Omkar M. Parkhi;Andrea Vedaldi;Andrew Zisserman.
british machine vision conference (2015)

3427 Citations

Spatial transformer networks

Max Jaderberg;Karen Simonyan;Andrew Zisserman;Koray Kavukcuoglu.
neural information processing systems (2015)

3273 Citations

Object retrieval with large vocabularies and fast spatial matching

J. Philbin;O. Chum;M. Isard;J. Sivic.
computer vision and pattern recognition (2007)

3043 Citations

Visual Reconstruction

Andrew Blake;Andrew Zisserman.
(1987)

2882 Citations

Object class recognition by unsupervised scale-invariant learning

R. Fergus;P. Perona;A. Zisserman.
computer vision and pattern recognition (2003)

2872 Citations

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