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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
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ResMem-Net: memory based deep CNN for image memorability estimation.

Arockia Praveen1, Abdulfattah Noorwali2, Duraimurugan Samiayya3

  • 1Phosphene AI, Madurai, India.

Peerj. Computer Science
|November 26, 2021
PubMed
Summary

We developed ResMem-Net, a novel deep learning model combining LSTM and CNN, to predict image memorability. This efficient architecture achieves state-of-the-art results, nearing human consistency in predicting what makes images memorable.

Keywords:
Deep LearningImage MemorabilityObject InterestingnessSaliencyVisual Emotions

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Image memorability prediction is challenging due to its subjective nature.
  • Deep learning advancements have enabled significant progress in predicting image memorability.
  • Existing models often lack efficiency and interpretability.

Purpose of the Study:

  • To propose a novel deep learning architecture, ResMem-Net, for accurate image memorability prediction.
  • To analyze the intrinsic properties contributing to image memorability using explainable AI techniques.
  • To develop a memory-efficient model suitable for production.

Main Methods:

  • Developed ResMem-Net, a hybrid LSTM-CNN architecture leveraging CNN hidden layer information.
  • Utilized GradRAM technique to generate heatmaps for visualizing learned visual emotions and saliency.
  • Trained and evaluated the model on the Large-scale Image Memorability (LaMem) dataset.

Main Results:

  • Achieved a rank correlation of 0.679 and a mean squared error of 0.011 on the LaMem dataset.
  • Outperformed current state-of-the-art models and demonstrated performance close to human consistency (p=0.68).
  • The proposed architecture has significantly fewer parameters than existing models, enhancing memory efficiency.

Conclusions:

  • ResMem-Net effectively predicts image memorability by integrating visual features and learned saliency.
  • The model provides insights into factors influencing image memorability, such as visual emotions.
  • The developed architecture is computationally efficient and practical for real-world applications.