Less is More: Visual Search without Image and User Ownership at the Edge

Professor Sean Gong

Queen Mary University of London

Visual search of unseen objects as a Zero-Shot Learning problem assumes the availability of at least one-shot query image depicting a representation of a search target. This assumption is limited when only a brief textual (or verbal) description of the search target is available whilst visual data is either unavailable or due to privacy protection, e.g. removal of facial imagery from public video footages. Deep learning has been hugely successful for computer vision tasks in recent years because of the accessibility of shared and centralised large sized training data pulled globally. However, increasing awareness of privacy concerns and a renewed focus on regional user-ownership of localised data poses new challenges to the conventional wisdom for centralised deep learning on big data, especially for improving human recognition tasks such as person reidentification.

In this talk, I will highlight challenges and recent progress on deep learning for text guided visual search without any query visual input, and decentralised learning from non-shared training data distributed at multiple user-locations having independent non-overlapping multi-domain labels. Both examples are generalisations of Zero-Shot Learning.


Shaogang Gong is Professor of Visual Computation at Queen Mary University of London (since 2001), a pioneer in computer vision research for visual surveillance and person re-identification, and for video analytics technology deployment in law enforcement video forensic analysis.

He served on the Steering Panel of the UK Government Chief Scientific Adviser’s Science Review.

Gong was a research fellow on the EU ESPRIT VIEWS (Visual Interpretation and Evaluation of Wide-area Scenes) in 1989-1993, the world’s first multinational collaborative computer vision project on visual surveillance in urban environments. He led the EU Security Programme SAMURAI (Suspicious and Abnormal Behaviour Monitoring Using a Network of Cameras for Situation Awareness Enhancement) that pioneered Person Re-Identification (RE-ID) in-the-wild for public infrastructure protection in 2008-2011. Between 2009-2013, he led the UK government project on developing a system for Multi-Camera Object Tracking by RE-ID funded by the UK INSTINCT Programme (Innovative Science and Technology in Counter-Terrorism), in collaboration with the BAE Systems. He won the 2019 Bruce Dickinson Entrepreneur of the Year Award, the 2019 Queen Mary Innovation Award, and the 2017 Queen Mary Academic Commercial Enterprise Award. A commercial system built on the patents and software from Gong’s research won the 2017 Global Frost & Sullivan Award for Technical Innovation for Law Enforcement Video Forensics Technology, and won the 2017 Aerospace Defence Security Innovation Award given by the UK Security Minister for “revolutionary solution to reviewing CCTV footage”.

Gong has authored and edited 7 books on Person Re-Identification, Visual Analysis of Behaviour, Video Analytics for Business Intelligence, Dynamic Vision from Images to Face Recognition, Analysis and Modelling of Faces and Gestures. His recent research has been on Zero-Shot Learning, Transfer Learning, Distributed Learning, Unsupervised and Semi-Supervised Deep Learning, Imbalanced Deep Learning, Deep Reinforcement Learning, Attention Deep Learning, and Human-In-The-Loop Active Learning.

Gong is a Turing Fellow of the Alan Turing Institute of Data Science and Artificial Intelligence, and was a Royal Society Research Fellow. He received his DPhil degree from Keble College, Oxford University in 1989, sponsored by GEC Hirst and the Royal Society. He is a Fellow of IEE (now IET), a Fellow of the British Computer Society, and a Member of the UK Computing Research Committee.