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A new method called FORCE achieves extreme sparsity in artificial neural networks by progressively removing up to 99.9% of parameters at initialization, making it a promising candidate for training networks on edge devices (like drones or smartphones).

Announcing 'kapture' the open source release of a new SfM and visual localization data format. Available for ECCV2020 workshop challenge on visual localization.

ACL 2020 was held virtually the week of July 6th. As a combined industry/academic research team, we grouped information and papers on the topics we considered emerging at this year's conference.

A overview of current research methods in lifelong learning (rehearse, grow, regularize), the advantages and drawback of each and where we see future work heading.

With Ambient Wanderer, we aim to inform the design of systems that can meet the requirements of on-the-go users looking to discover relevant points of interest, and to demonstrate the benefits of using a hybrid Wizard of Oz approach to investigate POI recommender systems.

A first-of-its-kind architecture that, based on a single image, predicts how a robot can pick up objects from within any scene could revolutionize applications in AR/VR and robotics.

1^st in autonomous vehicle challenge, 2^nd in local feature and 4^th in handheld devices R2D2 and KAPTURE demonstrate generalizability and wide variety of applications

Our navigation system enables robots to adapt to specific real-world environments and use cases with only small amounts of human preference data.

We're releasing a state-of-the-art multilingual and multi-domain neural machine translation model specialised for biomedical data that enables translation into English from five languages (French, German, Italian, Spanish and Korean).

A new platform based on deep-learning approaches to handwritten-text recognition and information extraction enables data from century-old documents to be parsed and analysed, making it possible to explore epidemics and the evolution of populations over time.