Paper: http://ceur-ws.org/Vol-2882/paper6.pdf
YouTube: https://youtu.be/ySGGu_4vaxs
Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin, Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu and Alan F. Smeaton : Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable? Proc. of MediaEval 2020, 14-15 December 2020, Online.
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video to Text dataset, containing more action rich video content as compare with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for the run submission.
Presented by: Rukiye Savran Kiziltepe
GenAI talk for Young at Wageningen University & Research (WUR) March 2024
Overview of MediaEval 2020 Predicting Media Memorability task: What Makes a Video Memorable?
1. MediaEval2020
Predicting Media Memorability
Task Overview
Alba García Seco de Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain, Mihai Gabriel Constantin,
Claire-Hélène Demarty, Faiyaz Doctor, Bogdan Ionescu, Alan Smeaton
Presentation Video
2. Task Description
Goal: predicting how memorable a video is to viewers
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• Automatically predicting short-term and
long-term memorability
• TRECVid 2019 Video to Text dataset1
• Sound and more action
1. Awad, G., Butt, A.A., Lee, Y., Fiscus, J., Godil, A., Delgado, A., Smeaton, A.F. and Graham, Y., Trecvid 2019:
An evaluation campaign to benchmark video activity detection, video captioning and matching, and video
search & retrieval. 2019.
3. Annotation Tool
• Short-term memorability : a few minutes after memorization
• Long-term memorability: 24 – 72 hours later
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Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. Proceedings of the IEEE
International Conference on Computer Vision. 2019.
Video Memorability Game
4. Annotation Protocol
Step 1 (180 videos)
• 40 targets– repeated after a few minutes
• 60 fillers – non target videos
• 20 vigilance fillers – repeated quickly to monitor the attention
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Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong, and Martin Engilberge. VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability. Proceedings of the IEEE
International Conference on Computer Vision. 2019.
Step 2 (120 videos)
• 40 targets– randomly chosen from non-vigilance fillers
• 80 fillers – randomly chosen new videos
5. Dataset Description
• TRECVid 2019
(Video to Text)
• 1500 videos
• 1000 training set
• 500 test set
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6. Dataset Description
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• AlexNetFC7
• HOG
• HSVHist
• RGBHist
• LBP
• VGGFC7
• C3D
• Text descriptions
• Annotations
• Response time
• Key press
• Video position
Short-term memorability score
Long-term memorability score
7. Examples (Low Short-term and Long-term Memorability)
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• At football game, the ball is kicked past end zone and
woman is knocked down from her knees
• football player are playing at a football field.
• At a college football game, during a kickoff, the kicker
kicks the ball over the endzone and hits a spectator
in the face while they are trying to catch it.
• a person is injured when the football player kicked a
ball across a field during a game
• Football kicks football during a day game and a
cheerleader tries to catch it and ball hits her in the
head.
8. Examples (High Short-term and Long-term Memorability)
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• Two boys wearing white shirts on playground swings
• Two young men, are on a swing and yell, outdoors.