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Found 10 papers, 1 papers with code
Overview of The MediaEval 2022 Predicting Video Memorability Task
This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022.
Experiences from the MediaEval Predicting Media Memorability Task
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.
Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD).
Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification
Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD).
Overview of The MediaEval 2021 Predicting Media Memorability Task
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.
An Annotated Video Dataset for Computing Video Memorability
Data includes the reaction times for each recognition of each video.
Face Verification with Challenging Imposters and Diversified Demographics
We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer.
Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?
This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task.
MediaEval 2018: Predicting Media Memorability Task
In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation.
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos
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
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