Search Results for author: Bogdan Ionescu

Found 10 papers, 1 papers with code

Experiences from the MediaEval Predicting Media Memorability Task

no code implementations7 Dec 2022 Alba García Deco de Herrera, Mihai Gabriel Constantin, Chaire-Hélène Demarty, Camilo Fosco, Sebastian Halder, Graham Healy, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Mushfika Sultana, Lorin Sweeney

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time.

Overview of The MediaEval 2021 Predicting Media Memorability Task

no code implementations11 Dec 2021 Rukiye Savran Kiziltepe, Mihai Gabriel Constantin, Claire-Helene Demarty, Graham Healy, Camilo Fosco, Alba Garcia Seco de Herrera, Sebastian Halder, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Lorin Sweeney

This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task.

EEG Electroencephalogram (EEG)

Face Verification with Challenging Imposters and Diversified Demographics

1 code implementation16 Oct 2021 Adrian Popescu, Liviu-Daniel Ştefan, Jérôme Deshayes-Chossart, Bogdan Ionescu

We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer.

Face Verification

MediaEval 2018: Predicting Media Memorability Task

no code implementations3 Jul 2018 Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do, France Rennes

In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation.

Benchmarking Memorization

Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos

no code implementations CVPR 2017 Ionut Cosmin Duta, Bogdan Ionescu, Kiyoharu Aizawa, Nicu Sebe

The proposed method addresses an important problem of video understanding: how to build a video representation that incorporates the CNN features over the entire video.

Action Recognition In Videos Temporal Action Localization +1

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