S
Sean Moran
Researcher at Huawei
Publications - 29
Citations - 507
Sean Moran is an academic researcher from Huawei. The author has contributed to research in topics: Computer science & Locality-sensitive hashing. The author has an hindex of 9, co-authored 25 publications receiving 336 citations. Previous affiliations of Sean Moran include University of Glasgow & University of Edinburgh.
Papers
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Proceedings ArticleDOI
Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media
Miles Osborne,Sean Moran,Richard McCreadie,Alexander von Lünen,Martin D. Sykora,Elizabeth Cano,Neil Ireson,Craig Macdonald,Iadh Ounis,Yulan He,Thomas Jackson,Fabio Ciravegna,Ann O'Brien +12 more
TL;DR: The capabilities of ReDites are demonstrated using an extended use case from the September 2013 Westgate shooting incident and it is shown that enriched events are made available for users to explore within seconds of that event occurring.
Proceedings ArticleDOI
DeepLPF: Deep Local Parametric Filters for Image Enhancement
TL;DR: A novel approach to automatically enhance images using learned spatially local filters of three different types, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Proceedings ArticleDOI
Sparse Kernel Learning for Image Annotation
Sean Moran,Victor Lavrenko +1 more
TL;DR: A sparse kernel learning framework for the Continuous Relevance Model (CRM) that greedily selects an optimal combination of kernels, which rapidly converges to an annotation accuracy that substantially outperforms a host of state-of-the-art annotation models.
Posted Content
CURL: Neural Curve Layers for Global Image Enhancement
TL;DR: A novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool, which produces state-of-the-art image quality versus recently proposed deep learning approaches in both objective and perceptual metrics.
Proceedings ArticleDOI
Enhancing First Story Detection using Word Embeddings
TL;DR: It is shown how word embeddings can be used to increase the effectiveness of a state-of-the art Locality Sensitive Hashing (LSH) based first story detection (FSD) system over a standard tweet corpus.