Oscar Koller

TL;DR: This work formalizes SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge) and allows to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language.

TL;DR: This work presents a statistical recognition approach performing large vocabulary continuous sign language recognition across different signers, and is the first time system design on a large data set with true focus on real-life applicability is thoroughly presented.

TL;DR: This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm, which allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos.

TL;DR: The results of an interdisciplinary workshop are presented, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.

TL;DR: A novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as “Sequence-to-sequence” learning) is proposed, which decompose the problem into a series of specialised expert systems referred to as SubUNets, and serves to significantly improve the performance of the overarching recognition system.