Understanding and Predicting Image Memorability at a Large Scale

@article{Khosla2015UnderstandingAP,
  title={Understanding and Predicting Image Memorability at a Large Scale},
  author={Aditya Khosla and Akhil S. Raju and Antonio Torralba and Aude Oliva},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={2390-2398}
}
Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data. Here, we introduce a novel experimental procedure to objectively measure human memory, allowing us to build LaMem, the largest annotated image memorability dataset to date (containing 60,000 images from diverse sources). Using Convolutional Neural Networks (CNNs), we show that fine-tuned deep features outperform all other features by a large margin, reaching a rank correlation… CONTINUE READING

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