News for Summer 2018

NEWS FLASH: BICV to BICI
We are undergoing a (small) shift in branding. This shift reflects recent developments in the field of deep learning, computer vision and artificial intelligence. The change in group title better reflects some of our recent work which deals with inference problems that are not necessarily related to the field of computer vision.

Our new website can be found here; this [BICV] website will continue running until good ol’ bg-nightcrawler finally packs it in. (Those of you who have coaxed bg-nightcrawler into continued life will know what I mean….)

  • First year PhD candidate Tianhong Dai is giving a tutorial presentation on PyTorch at the Alan Turing Institute on PyTorch (August, 2018).
  • Congrats to Alison Pouplin and Katarzyna Janocha on their poster presentation at meets Medical Imaging meets Deep Learning; you can find the poster here.
  • Congratulations to Antonia Creswell for acceptance of Denoising Adversarial Autoencoders to the IEEE Transactions on Neural Networks.
  • Congrats to part-time PhD candidate (and research associate) Letizia Gionfrida and Shanxin Yuan for winning the MedTechSuperConnector grant.
  • Congrats to Dr Zehra Uslu (PhD graduate) on the paper accepted at MICCAI 2018.
  • Congratulations to Dr Yumnah Muhamied on the abstract accepted for Proceedings of the 14th Annual Conference of the European Cardiac Arrhythmia Society; a longer paper is in the works as well.

News for March, 2017

Preprint A new paper on Denoising Adversarial Autoencoders is now available on arXiv here.  Code is also available from Toni Creswell on GitHub

Jobs Through collaborations with the DSI, Computing, Aeronautics, the Medical School and Royal College of Music, there are several job opportunities opening up. Please drop us a line at a.bharath@imperial.ac.uk for informal enquiries.

 

News for February

Rosetrees Trust Award   We have recently received a grant that supports the use of machine learning in cardiovascular risk prediction and treatment planning. This will fund joint research between the ElectroCardioMaths Group, the Aeronautics Department, the Data Sciences Institute and Bioengineering.

DL4I&VU Workshop  We are hosting – together with the Cortexica, the Data Sciences Institute and Neurotechnology CDT – a workshop on Deep Learning for Image and Video Understanding (Feb 24th. 2017).  Future workshops will follow.

News for October

Congratulations to Toni Creswell on her presentation at the VISArt 2016 Conference in Amsterdam. The paper is available on arXiv.

Join us on 3rd November @ Big Data London, 20 Air Street.  AAB will be on the Panel for Deep Learning.

20th October: AAB gave a talk at IDEALondon on Deep Architectures: Past Present and Future.  Slides will soon be available.

News for August

  • Congratulations to Toni Creswell on her paper “Adversarial Training for Sketch Retrieval”, which will appear at an ECCV Workshop; a preprint is available on arXiV.
  • Congratulations to Christoforos Charalambous on his paper “A data augmentation methodology for training machine/deep learning gait recognition algorithms”, at the British Machine Vision Conference.
  • Also this month: “An assistive haptic interface for appearance-based indoor navigation”, is available from here.

Congratulations to Kai & Nat on IJCAI Workshop Paper

Congratulations to Kai Arulkumaran and Nat Dilokthanakul on getting their paper accepted for the 2016 IJCAI Workshop on Deep Reinforcement Learning: Frontiers and Challenges. The paper integrates deep reinforcement learning and hierarchical reinforcement learning by using multiple deep Q-network heads to represent different option policies, with shared convolutional layers to learn common statistical relationships from raw visual inputs. The paper can be viewed on arXiv.

Paper accepted for CVIU

Congratulations to Jose and team for getting to the CVIU Special Issue on computer vision for assistive devices. The paper examines the components of a prototype assistive system for navigation. We look at the accuracy of spatial localization using computer vision, comparing it to the ability of users to sense location via cues provided via a haptic tablet.