# Adaptive Multi-Modal Ensemble Network for Video Memorability Prediction
## 2 Related Works  
In this section, we summarize the previous works on media memorability and related concepts of media memorability. The work of [PERSON] et al. [2] led to a pioneering study of media memorability. To measure image memorability, [PERSON] et al. first proposed the memorability score of each image by the memory game. Based on the visual memory game, [PERSON] et al. [2] proved that image memorability is the intrinsic property of each image and showed that the image memorability score has sufficient consistency of each image across various viewers by the consistency analysis. With the development of research on image memorability, more researchers have devoted themselves to studying what makes the image memorable and how to utilize machine learning algorithms to predict image memorability scores. [PERSON] et al. [11] further explored the fact that image features, attributes and labels have a positive effect on the memorability of an image and use the support vector regression algorithm to predict image memorability scores. Intrinsic and extrinsic properties [12] that make media information memorable have been studied in recent years. Moreover, objects, emotions, saliency, and aesthetics contribute to making an image memorable [13; 14; 15; 16]. It has to be mentioned that [PERSON] et al. [17] specifically discussed what makes an object memorable. To better understand what makes an image forgettable or memorable, [PERSON] et al. [14] were committed to studying the role of depth and motion and showed that depth and motion are helpful. Although many properties have been mined related to the memorability of the image, researchers only use traditional and simple image features, such as Pixels, GIST [2; 11; 18], to predict image memorability. In other words, much of the image information has not been mined and utilized. Contrast to that, scholars attempted to extract deep features from deep learning algorithms to predict image memorability with the deep learning algorithm becoming extremely popular in the 2010s [19; 20]. [PERSON] et al. [21] first used fine-tuned Hybrid Convolutional Neural Networks (CNNs) [22] to extract deep features, and the performance outperformed all other features at that time. Then, [PERSON] et al. [23] used three common convolutional networks types to derive deep features from predicting image memorability and they drew a conclusion that deep features outperformed traditional features which are universally used, such as the SIFT, SSIM, and HOG\({}_{2\times 2}\). Due to the application of deep learning, [PERSON] et al. [24] presented their computation model, which is based on a deep learning frame to predict memorability scores while support vector regression is used in previous studies. When predicting image memorability, several different datasets and ground truth have been constructed by various scholars. [PERSON] et al. [2] established the Scene UNderstanding (SUN) memorability dataset and the Large-scale Memorability (LaMem) dataset was built by [PERSON] et al. [21]. SUN memorability and the LaMem dataset are also the most used in further research. The point of adaptive semi-supervised feature selection [25] is to attempt to apply memorability. [PERSON] et al. [18] proposed the concept of Visual Memory Schema (VMS) and built the VISCHEMA image set. Based on these datasets, more research on image memorability will be carried out in the future. As we have mentioned before, different media leave different impressions on people, some of which can be remembered, while others are ignored. Getting inspired by the memorability of the image, video memorability is studied gradually, and video memorability scores are defined as the percentage of correct detection of each video by different participants [3]. The research on image memorability can be said to be the cornerstone of other media memorability research. In the paper by [PERSON] et al. [26], they contributed to face memorability. [PERSON] et al. [27] raised a video hashing method based on memorability features.