#23: MLDublin meets ADAPT @ DogPatch Labs

- 1 min
Marco Forte PhD researcher with Sigmedia Group, the ADAPT Centre in Trinity College Dublin
Using deep learning to bypass the greenscreen

Replacing the background behind an actor with the use of a greenscreen is a common task in the film and television industry. However, in the rise of amateur user generated content on YouTube, and video messaging the greenscreen proves to be a undesirable and impractical constraint. By leveraging deep learning we are able to separate foreground and background elements in a natural environment without large user interaction thanks to semantic understanding of a scene. In this talk I give a brief overview of greenscreen and natural image keying and it's relation the field of semantic segmentation. I will discuss in particular recent work of ours, submitted to ICIP, where we experiment with the benefits of a multi-task objective for the task of natural image keying and the possibilities we open for future work.

Procheta Sen PhD researcher with the ADAPT Centre in Dublin City University
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction

Elias Giacoumidis Marie-Curie Fellow at CONNECT in Dublin City University
Machine learning optical fiber telecommunications

Machine learning has emerged as a new clever way for optimizing and improving the performance of fiber-optic telecommunication systems by tackling both deterministic and stochastic noises in the network without increasing complexity. The potential of developing new modems incorporating machine learning technology to provide consistently high-speed broadband connectivity is an exciting new research topic. Digital signal processing (DSP)-based machine learning bridges the gap between all-optical high-resolution signal processing and DSP. Harnessing appropriate machine learning algorithms we can successfully compensate nonlinear effects in electronic domain to increase transmission-reach of modern high-capacity optical systems.

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