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/
Most Recent Publications
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images
CASSPR: Cross Attention Single Scan Place Recognition
CASSPR: Cross Attention Single Scan Place Recognition
RbA: Segmenting Unknown Regions Rejected by All
RbA: Segmenting Unknown Regions Rejected by All
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
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
Most Recent Publications
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images
LoCUS: Learning Multiscale 3D-consistent Features from Posed Images
CASSPR: Cross Attention Single Scan Place Recognition
CASSPR: Cross Attention Single Scan Place Recognition
RbA: Segmenting Unknown Regions Rejected by All
RbA: Segmenting Unknown Regions Rejected by All
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
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
