# AMNet: Memorability Estimation with Attention

arXiv: https://arxiv.org/abs/1804.03115 · HTML: https://mlsanity.com/paper/1804.03115/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [A Circular-Structured Representation for Visual Emotion Distribution Learning](https://mlsanity.com/paper/2106.12450/) (2021)
- [Embracing New Techniques in Deep Learning for Estimating Image Memorability](https://mlsanity.com/paper/2105.10598/) (2021)
- [Long-Term Ad Memorability: Understanding & Generating Memorable Ads](https://mlsanity.com/paper/2309.00378/) (2023)
- [Memorability: An image-computable measure of information utility](https://mlsanity.com/paper/2104.00805/) (2021)
- [What Images are More Memorable to Machines?](https://mlsanity.com/paper/2211.07625/) (2022)
- [An Efficient Style Virtual Try on Network for Clothing Business Industry](https://mlsanity.com/paper/2105.13183/) (2021)
- [Controlling Memorability of Face Images](https://mlsanity.com/paper/2202.11896/) (2022)
- [Don't Judge Before You CLIP: A Unified Approach for Perceptual Tasks](https://mlsanity.com/paper/2503.13260/) (2025)
- [Analysing the Memorability of a Procedural Crime-Drama TV Series, CSI](https://mlsanity.com/paper/2208.03479/) (2022)
- [Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification](https://mlsanity.com/paper/2301.06080/) (2022)
- [Feature representations useful for predicting image memorability](https://mlsanity.com/paper/2303.07679/) (2023)
- [Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability](https://mlsanity.com/paper/2311.16484/) (2023)
- [Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise](https://mlsanity.com/paper/2501.15890/) (2025)
- [Enhancing Video Memorability Prediction with Text-Motion Cross-modal Contrastive Loss and Its Application in Video Summarization](https://mlsanity.com/paper/2506.08649/) (2025)
- [How to Take a Memorable Picture? Empowering Users with Actionable Feedback](https://mlsanity.com/paper/2602.21877/) (2026)
- [Human-Inspired Context-Selective Multimodal Memory for Social Robots](https://mlsanity.com/paper/2604.12081/) (2026)

## Abstract (arXiv)

In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency.