Timothy Hospedales

(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, and 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 learinng approaches. I have looked at a variety application areas including computer vision (behaviour understanding, person re-identification, attribute and zero-shot learning), robotics, sensor fusion, novel human-computer interfaces, computational social sciences, theoretical neuroscience and business data analytics.


05/2014: EPSRC 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 Oral: Our Sketch recognition Deep Neural Network is the first to surpass human performance!

09/2015: Our BMVC'15 sketch recognition paper wins Best Science Paper prize!

02/2016: Expert Systems with Applications paper accepted

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

07/2016: Joining IET Computer vision as associate editor

09/2016: Paper accepted to EMNLP'16!

09/2016: Moved from QMUL to IPAB at UoE

11/2016: Paper in AAAI'17!

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!

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.

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

Visit my Google Scholar Profile.












Lifelong and Transfer Learning

Zero-Shot Learning

Active Learning

Behaviour and Action Understanding

Person re-identification

Visual Attribute Learning

Deep Learning

Machine Learning

Zero-shot learning, Vision and Language

Computer Vision




Chapter / Workshop / Other


  • Person Re-identification, Attributes, Transfer Learning

PhD Students

  • Lifelong Learning, Deep Learning
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

PhD Students, Co-supervisor

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

PhD Students, Visiting

  • Structured Video Analysis, Generative Adversarial Nets

PhD Students, Graduated

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


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