| LaMem: A Large-Scale Dataset for Image Memorability |
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| In this package, we include the images and their memorability scores, after accounting |
| for false alarms. The images are available in the 'images/' folder and the memorability |
| scores are available in the 'splits/' folder. In the splits folder, we provide information |
| for 5 splits of the data, each consisting of the train, val and test sets (e.g., train_1.txt, |
| val_1.txt, and test_1.txt). |
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| In each txt file, we provide scores in the following way: |
| <image_name> <memorability_score> |
| where <image_name> has values like 00000001.jpg and <memorability_score> is a float |
| value ranging from 0 to 1. |
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| Note that a particular image can have different scores in different splits because for |
| each split of the data, we use a random half of the participants to find the train and |
| validation memorability scores and the other half to find the test scores i.e., both the |
| images and participants for each of the splits is disjoint. This setting is the same as |
| that of prior work. |
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| Please cite the following paper if you use this dataset in your work: |
| Understanding and Predicting Image Memorability at a Large Scale |
| Aditya Khosla, Akhil S. Raju, Antonio Torralba and Aude Oliva |
| International Conference on Computer Vision (ICCV), 2015 |
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| The bibtex file is available via the project website: http://memorability.csail.mit.edu |
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| For any questions or comments, please contact Aditya Khosla (khosla@csail.mit.edu). |
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| Please read the provided LICENSE file for the terms of use of this data. |
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| Copyright (c) 2015 Aditya Khosla |
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