| LaMem: A Large-Scale Dataset for Image Memorability | |
| 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). | |
| 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. | |
| 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. | |
| 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 | |
| The bibtex file is available via the project website: http://memorability.csail.mit.edu | |
| For any questions or comments, please contact Aditya Khosla (khosla@csail.mit.edu). | |
| Please read the provided LICENSE file for the terms of use of this data. | |
| Copyright (c) 2015 Aditya Khosla | |