H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 57 Citations 12,973 188 World Ranking 1965 National Ranking 106

Research.com Recognitions

Awards & Achievements

2018 - IAPR P. Zamperoni Award Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

His primary areas of investigation include Artificial intelligence, Machine learning, Sketch, Artificial neural network and Deep learning. His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition. His Pattern recognition research integrates issues from Object and Feature.

In general Machine learning study, his work on Unsupervised learning often relates to the realm of Public space, thereby connecting several areas of interest. Timothy M. Hospedales interconnects Network architecture and Parameterized complexity in the investigation of issues within Artificial neural network. His studies deal with areas such as Facial recognition system, Face, Generalization, Convolutional neural network and Three-dimensional face recognition as well as Deep learning.

His most cited work include:

  • Learning to Compare: Relation Network for Few-Shot Learning (1156 citations)
  • Deep Mutual Learning (440 citations)
  • Deeper, Broader and Artier Domain Generalization (293 citations)

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

Timothy M. Hospedales focuses on Artificial intelligence, Machine learning, Sketch, Pattern recognition and Embedding. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Natural language processing. His work in Machine learning tackles topics such as Inference which are related to areas like Generative model.

His research in Sketch intersects with topics in Key and Image retrieval. His Pattern recognition study incorporates themes from Annotation, Representation and Automatic image annotation. His biological study spans a wide range of topics, including Word and Theoretical computer science.

He most often published in these fields:

  • Artificial intelligence (75.35%)
  • Machine learning (37.32%)
  • Sketch (21.13%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (75.35%)
  • Machine learning (37.32%)
  • Sketch (21.13%)

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

Timothy M. Hospedales spends much of his time researching Artificial intelligence, Machine learning, Sketch, Reinforcement learning and Pattern recognition. His work on Deep learning, Leverage and Feature extraction as part of general Artificial intelligence research is frequently linked to Meta learning and Scale, thereby connecting diverse disciplines of science. His Deep learning research is multidisciplinary, incorporating perspectives in Transfer of learning, Artificial neural network, Data science and Pattern recognition.

His study in the field of Feature learning is also linked to topics like Focus. The various areas that Timothy M. Hospedales examines in his Sketch study include Generator, Embedding, Key and Image retrieval. His Reinforcement learning study combines topics in areas such as Optimization problem and Selection.

Between 2019 and 2021, his most popular works were:

  • Meta-Learning in Neural Networks: A Survey (120 citations)
  • Incremental Few-Shot Object Detection (24 citations)
  • Deep Domain-Adversarial Image Generation for Domain Generalisation (21 citations)

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

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Artificial intelligence, Machine learning, Meta learning, Reinforcement learning and State are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Sketch and Pattern recognition. His study explores the link between Sketch and topics such as Image retrieval that cross with problems in Information retrieval.

His Machine learning research is multidisciplinary, incorporating elements of Object detection and Feature extraction. His Reinforcement learning research focuses on Transfer of learning and how it connects with Human–computer interaction and Developmental robotics. The concepts of his State study are interwoven with issues in Perspective and Adaptation.

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

Learning to Compare: Relation Network for Few-Shot Learning

Flood Sung;Yongxin Yang;Li Zhang;Tao Xiang.
computer vision and pattern recognition (2018)

708 Citations

Person Re-identification by Attributes.

Ryan Layne;Timothy M. Hospedales;Shaogang Gong.
british machine vision conference (2012)

359 Citations

Deeper, Broader and Artier Domain Generalization

Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales.
international conference on computer vision (2017)

293 Citations

Transductive Multi-View Zero-Shot Learning

Yanwei Fu;Timothy M. Hospedales;Tao Xiang;Shaogang Gong.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2015)

277 Citations

Deep Mutual Learning

Ying Zhang;Tao Xiang;Timothy M. Hospedales;Huchuan Lu.
computer vision and pattern recognition (2018)

275 Citations

A Markov Clustering Topic Model for mining behaviour in video

Timothy Hospedales;Shaogang Gong;Tao Xiang.
international conference on computer vision (2009)

251 Citations

Sketch Me That Shoe

Qian Yu;Feng Liu;Yi-Zhe Song;Tao Xiang.
computer vision and pattern recognition (2016)

201 Citations

Learning to generalize: Meta-learning for domain generalization

Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales.
national conference on artificial intelligence (2018)

200 Citations

Transferring a semantic representation for person re-identification and search

Zhiyuan Shi;Timothy M. Hospedales;Tao Xiang.
computer vision and pattern recognition (2015)

200 Citations

Multi-level Factorisation Net for Person Re-identification

Xiaobin Chang;Timothy M. Hospedales;Tao Xiang.
computer vision and pattern recognition (2018)

193 Citations

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Best Scientists Citing Timothy M. Hospedales

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Tao Xiang

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Qi Tian

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Huawei Technologies (China)

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Dacheng Tao

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Zeynep Akata

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Xiaogang Wang

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Chinese University of Hong Kong

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Bernt Schiele

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Max Planck Institute for Informatics

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Sridha Sridharan

Sridha Sridharan

Queensland University of Technology

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Zhejiang University

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Nanyang Technological University

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Clinton Fookes

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Barbara Caputo

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