VideoMem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability

Humans share a strong tendency to memorize/forget some of the visual information they encounter. This paper focuses on understanding the intrinsic memorability of visual content. To address this challenge, we introduce a large scale dataset (VideoMem) composed of 10,000 videos with memorability scor...

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Bibliographic Details
Published in2019 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 2531 - 2540
Main Authors Cohendet, Romain, Demarty, Claire-Helene, Duong, Ngoc, Engilberge, Martin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Atmospheric measurements
Particle measurements
Predictive models
Protocols
Task analysis
Time measurement
Visualization
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Summary:Humans share a strong tendency to memorize/forget some of the visual information they encounter. This paper focuses on understanding the intrinsic memorability of visual content. To address this challenge, we introduce a large scale dataset (VideoMem) composed of 10,000 videos with memorability scores. In contrast to previous work on image memorability - where memorability was measured a few minutes after memorization - memory performance is measured twice: a few minutes and again 24-72 hours after memorization. Hence, the dataset comes with short-term and long-term memorability annotations. After an in-depth analysis of the dataset, we investigate various deep neural network-based models for the prediction of video memorability. Our best model using a ranking loss achieves a Spearman's rank correlation of 0.494 (respectively 0.256) for short-term (resp. long-term) memorability prediction, while our model with attention mechanism provides insights of what makes a content memorable. The VideoMem dataset with pre-extracted features is publicly available.
ISSN:2380-7504
DOI:10.1109/ICCV.2019.00262