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Deep learning for predicting image memorability

Citation Author(s):
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty
Submitted by:
Ngoc Duong
Last updated:
25 April 2018 - 4:30am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters Name:
Ngoc Q. K. Duong
Paper Code:
ICASSP180014

Abstract 

Abstract: 

Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem. Experiment result shows that the proposed computational model obtains better prediction performance than the state of the art, and even outperforms human consistency. We further investigate the genericity of our model on other memorability datasets. Finally, by validating the model on interestingness datasets, we reconfirm the uncorrelation between memorability and interestingness of images.

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