Details

Autor(en) / Beteiligte

• Cohendet, Romain
• Demarty, Claire-Helene
• Duong, Ngoc
• Engilberge, Martin

Titel

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

Ist Teil von

• 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p.2531-2540

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.