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Thom Building, Department of Engineering Science

Dr

João Henriques RAEng

Principal Investigator

Research Fellow of the Royal Academy of Engineering

Biography

Dr. João Henriques is a Research Fellow of the Royal Academy of Engineering, working at the Visual Geometry Group (VGG) at the University of Oxford. His research focuses on computer vision and deep learning, with the goal of making machines more perceptive, intelligent and capable of helping people. He created the KCF and SiameseFC visual object trackers, which won the highly competitive VOT Challenge twice, and are widely deployed in consumer hardware, from Facebook apps to commercial drones. His research spans many topics: robot mapping and navigation, including reinforcement learning and 3D geometry; multi-agent cooperation and "friendly" AI; as well as various forms of learning, from self-supervised, causal, and meta-learning, including optimisation theory. For the latest research please refer to: https://www.robots.ox.ac.uk/~joao/

Information Engineering webpage

Most Recent Publications

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

SNeS: learning probably symmetric neural surfaces from incomplete data

SNeS: learning probably symmetric neural surfaces from incomplete data

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Learning altruistic behaviours in reinforcement learning without external rewards

Learning altruistic behaviours in reinforcement learning without external rewards

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Most Recent Publications

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

SNeS: learning probably symmetric neural surfaces from incomplete data

SNeS: learning probably symmetric neural surfaces from incomplete data

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable planners

Learning altruistic behaviours in reinforcement learning without external rewards

Learning altruistic behaviours in reinforcement learning without external rewards

View all