You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Humans share a strong tendency to memorize forget some of the visual information they encounter. This paper focuses on providing computational models for the prediction of the intrinsic memorability of visual content. To address this new challenge, we introduce a large scale dataset (VideoMem) composed of 10,000 videos annotated with memorability scores. In contrast to previous work on image memorability -- where memorability was measured a few minutes after memorization -- memory performance is measured twice: a few minutes after memorization and again 24-72 hours later. Hence, the dataset comes with short-term and long-term memorability annotations. After an in-depth analysis of the dataset, we investigate several deep neural network based models for the prediction of video memorability. Our best model using a ranking loss achieves a Spearman's rank correlation of 0.494 for short-term memorability prediction, while our proposed model with attention mechanism provides insights of what makes a content memorable. The VideoMem dataset with pre-extracted features is publicly available.

Cited Abstract(s):
[1]: When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite of this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper we focus on the problem of predicting how memorable an image will be. We show that memorability is a stable property of an image that is shared across different viewers. We introduce a database for which we have measured the probability that each picture will be remembered after a single view. We analyze image features and labels that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. Whereas making memorable images is a challenging task in visualization and photography, this work is a first attempt to quantify this useful quality of images.

[2]: Recent studies in image memorability showed that the memorability of an image is a measurable quantity and is closely correlated with semantic attributes. However, the intrinsic characteristics of memorability are not yet fully understood. It has been reported that in contrast to a popular belief unusualness or aesthetic beauty of the image may not be positively correlated with the image memorability. This counter-intuitive characteristic of memorability hinders a better understanding of image memorability and its applicability. In this paper, we investigate two new spatial features that are closely correlated with the image memorability yet intuitively explainable. We propose the Weighted Object Area (WOA) that jointly considers the location and size of objects and the Relative Area Rank (RAR) that captures the relative unusualness of the size of objects. We empirically demonstrate their useful correlation with the image memorability. Results show that both WOA and RAR can improve the memorability prediction. In addition, we provide evidence that the RAR can effectively capture object-centric unusualness of size.

[3]: 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 of 0.64, near human consistency (0.68). Analysis of the responses of the high-level CNN layers shows which objects and regions are positively, and negatively, correlated with memorability, allowing us to create memorability maps for each image and provide a concrete method to perform image memorability manipulation. This work demonstrates that one can now robustly estimate the memorability of images from many different classes, positioning memorability and deep memorability features as prime candidates to estimate the utility of information for cognitive systems. Our model and data are available at: http: memorability.csail.mit.edu.

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[7]: Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem. Experiment result shows that the proposed computational model obtains better prediction performance than the state of the art, and even outperforms human consistency. We further investigate the genericity of our model on other memorability datasets. Finally, by validating the model on interestingness datasets, we reconfirm the uncorrelation between memorability and interestingness of images.

[8]: Image memorability prediction is a recent topic in computer science. First attempts have shown that it is possible to computationally infer from the intrinsic properties of an image the extent to which it is memorable. In this paper, we introduce a fine-tuned deep learning-based computational model for image memorability prediction. The performance of this model significantly outperforms previous work and obtains a 32.78 relative increase compared to the best-performing model from the state of the art on the same dataset. We also investigate how our model generalizes on a new dataset of 150 images, for which memorability and affective scores were collected from 50 participants. The prediction performance is weaker on this new dataset, which highlights the issue of representativity of the datasets. In particular, the model obtains a higher predictive performance for arousing negative pictures than for neutral or arousing positive ones, recalling how important it is for a memorability dataset to consist of images that are appropriately distributed within the emotional space.

[9]: 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.

[10]: When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stick in our minds while others are quickly forgotten. In this paper, we focus on the problem of predicting how memorable an image will be. We show that memorability is an intrinsic and stable property of an image that is shared across different viewers, and remains stable across delays. We introduce a database for which we have measured the probability that each picture will be recognized after a single view. We analyze a collection of image features, labels, and attributes that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. While making memorable images is a challenging task in visualization, photography, and education, this work is a first attempt to quantify this useful property of images.
Focusing on the work on IM in computer vision, most studies made use of one of the two available large datasets, specifically designed for IM prediction, where IM was measured a few minutes after memorization [1] [3] , and consequently focused on predicting a so-called short-term IM [4] [2] [5] [3] [6]1 [6] [7] [9] . The pioneering work of [1] focused primarily on building computational models to predict IM from low-level visual features [1] , and showed that IM can be predicted to a certain extent. Several characteristics have also been found to be relevant for predicting memorability in subsequent work, for example saliency [4] , interestingness and aesthetics [10] , or emotions [3] . The best results were finally obtained by using fine-tuned or pre-extracted deep features, which outperformed all other features [3] [6]1 [7] [9] , with models achieving a Spearman's rank correlation near human consistency ( @math ) when measured for the ground truth collected in [1] [3] .