Timothy Hospedales

Reader
(Associate Professor)

Institute of Perception, Action and Behaviour (IPAB)
School of Informatics
The University of Edinburgh
t.hospedales at ed.ac.uk

I am a Reader within IPAB in the School of Informatics at the University of Edinburgh, where I head the Machine Intelligence Research group. I am also Visiting Reader at Queen Mary University of London. Previously I was Senior Lecturer/Lecturer ('12-16) at QMUL within the Risk and Information Management (RIM) group and Centre for Intelligent Sensing, where I founded the Applied Machine Learning Lab. I recieved my PhD in Neuroinformatics from Edinburgh in 2008, working with Sethu Vijayakumar in the Statistical Machine Learning and Motor Control group, and my BA in Computer Science from the University of Cambridge in 2002.

My research focuses on machine learning, particularly life-long transfer and active learning, with both probabilistic and deep learning approaches. I have looked at a variety application areas including computer vision (behaviour understanding, person re-identification, attribute and zero-shot learning), vision and language, robotics, sensor fusion, computational social sciences, theoretical neuroscience and business data analytics.

News


05/2014: EPSRC First Grant Awarded

07/2014: 2 papers on Open World Re-identification

01/2015: EU Horizon 2020 Grant DREAM awarded

05/2015: UAI 2015 paper on Bayesian Net transfer!

07/2015: BMVC 2015: Our Sketch recognition CNN is the first to surpass human performance, wins Best Science Paper prize!

03/2016: Three CVPR'16 papers accepted including one Oral!

09/2016: Paper at EMNLP'16: Distribution based Zero-Shot

11/2016: Paper in AAAI'17: Guaranteeing rationality in neural networks!

01/2017: Deep Multi-task learning in ICLR'17!

04/2017: Transfer learning for Control paper accepted in IJCAI'17!

07/2017: Three papers accepted to ICCV'17!

10/2017: Program co-chair of BMVC2018!

11/2017: EPSRC ORCA Hub in Robotics and AI awarded!

11/2017: Paper at AAAI'18: Meta-learning for domain generalization!

02/2018: Nine papers accepted at CVPR'18!

07/2018: Our Meta Learning Active Learning paper awarded Best Paper Prize at ICML AutoML'18!

07/2018: Three papers accepted at ECCV'18!

08/2018: Congratulations Kunkun Pang, Dynamic Ensemble Active Learning wins Best Student Paper Award @ ICPR2018!

11/2018: AAAI-19 paper accepted: transfer learning with disjoint label space

08/2016: Co-chairing BMVA symposium on Transfer Learning in Computer Vision

09/2016: Guest Editor - IET CV - Special Issue on Deep Learning for Computer Vision

10/2016: Keynote on transfer learning with paramaterised tasks and domains at ECCV 2016 TASK-CV workshop!

10/2016: Giving the zero-shot learning part of ACM Multimedia '16 tutorial Emerging topics in learning from noisy and missing data.

10/2017: Program co-chair of BMVC 2018

11/2017: Hiring one 3.5yr Postdoc on Machine Learning for Robotics & AI. Closing date: 14th Dec 2017. Advert. Apply.

03/2018: Keynote at Vision & Language Conference.

03/2018: Talk on sanity guarantees for deep learning at Deep Learning in Finance Summit London 2018.

09/2018: Hiring a postdoc research associate on deep learning, 2.5yr post, international applicants welcome. Closing date 4th October 2018. Apply.

Sample code for active learning & discovery (PAKDD'11,TKDE'11,ECCV'12)

Unsegmented sports news dataset available (ICDM'11). Includes reference extracted STIP features and ground-truth multi-label annotation.

Sample code for MCTM (ICCV'09, IJCV'11) and WSJTM (ACCV'10, PAMI'11)

Attribute Dataset for Heterogeneous Face Recognition (ACCV'14)

Code and Data for BMVC04 Fine-grained SBIR paper.

02/2015: Code for the famous ELF descriptor in re-id. First ever public release!

06/2015: Ground-truth Anomaly Annotation for ICCV'09 and IJCV'11 papers.

07/2015: Code for Sketch-a-Net BMVC'15 paper.

09/2015: Code for our ICLR'15 Multi-Task/Multi-domain paper

09/2015: Multi-camera video surveillance dataset from our TCSVT'15 paper

03/2016: 800K image+social metadata dataset from our ACM DH'16 paper

05/2017: LIMA dataset for re-id and tracking at CVPR'17

01/2018: Demo code CVPR'18 learning to compare few-shot learning

07/2018: Neural Decision tree Example Code.

Visit my Google Scholar Profile.

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

Lifelong and Transfer Learning

Zero-Shot Learning

Finance

Active Learning

Behaviour and Action Understanding

Person re-identification

Visual Attribute Learning

Deep Learning

Machine Learning

Zero-shot learning, Vision and Language

Sketches

Computer Vision

Neuroscience

Journal

Conference

Chapter / Workshop / Other

Postdocs

  • Lifelong Learning, Deep Learning, Meta-Learning

PhD Students

Carl Allen (CDT)
Ivana Balazevic (CDT)
Kunkun Pang
  • Active Learning & Perception
Tanmoy Mukherjee
  • Vision & Language, Semantic Embeddings
Chenyang Zhao
  • Lifelong learning for Robotics and Control
Marija Jegorova
  • Lifelong learning for Robotics and Control
Xueting Zhang
Boyan Gao

PhD Students, Co-supervisor

  • Cross-domain Transfer Learning
  • Cross-domain, Sketches, Face Recognition

PhD Students, Visiting

  • Structured Video Analysis, Generative Adversarial Nets
  • Person Identification
  • Generative, Graphical Models in Vision

Postdocs, Alum

  • Person Re-identification, Transfer Learning


PhD Students, Graduated

  • Lifelong Learning, Deep Learning
  • Transfer Learning, Zero-shot Learning, Attributes, Learning to Rank
  • → Faculty at Fudan University, PRC
  • Person Re-identification, Attributes
  • Transfer Learning in Bayesian Networks
  • → Goldsmiths → Faculty at NUDT, PRC
  • Weakly Supervised Learning, Attributes
  • → Imperial College London
  • Cross-domain matching, Sketches, SBIR
  • Visual Surveillance, Action Recognition, Transfer Learning
  • → National University of Singapore

Classes

2018/19: Semester A
  • Introduction to Vision and Robotics, Image and Vision Computing
2017/18: Semester A
  • Introduction to Vision and Robotics, Image and Vision Computing
2016/17: Semester B
  • Inf1 - Object Oriented Programming
2015/16: Semester A
  • Data Mining, Machine Learning, Procedural Programming
2014/15: Semester A
  • Data Mining, Procedural Programming
2014/15: Semester B
  • Java (BUPT)
2013/14: Semester A
  • Data Mining
2013/14: Semester B
  • Semantic Web
2012/13: Semester A
  • Information Systems
2012/13: Semester B
  • Semantic Web