Search Results for author: Dimitrios Mavroeidis

Found 6 papers, 1 papers with code

Untrimmed Action Anticipation

no code implementations8 Feb 2022 Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, Giovanni Maria Farinella

Experiments show that the performance of current models designed for trimmed action anticipation is very limited and more research on this task is required.

Action Anticipation Action Detection

Anomaly Detection for imbalanced datasets with Deep Generative Models

no code implementations2 Nov 2018 Nazly Rocio Santos Buitrago, Loek Tonnaer, Vlado Menkovski, Dimitrios Mavroeidis

We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data.

Anomaly Detection

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning

no code implementations5 Apr 2018 Stojan Trajanovski, Dimitrios Mavroeidis, Christine Leon Swisher, Binyam Gebrekidan Gebre, Bastiaan S. Veeling, Rafael Wiemker, Tobias Klinder, Amir Tahmasebi, Shawn M. Regis, Christoph Wald, Brady J. McKee, Sebastian Flacke, Heber MacMahon, Homer Pien

Importance: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and it has been recently demonstrated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate.

Computed Tomography (CT)

Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines

no code implementations11 Jul 2017 Sergio Consoli, Jacek Kustra, Pieter Vos, Monique Hendriks, Dimitrios Mavroeidis

We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets.

Multiscale Event Detection in Social Media

no code implementations25 Apr 2014 Xiaowen Dong, Dimitrios Mavroeidis, Francesco Calabrese, Pascal Frossard

In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data.

Clustering Event Detection

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