Learning Memorability Preserving Subspace for Predicting Media Memorability

In the following sections, we survey the subspace leaning methods applied to background modeling: Principal Components Analysis, Independent Component Analysis ,

After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video

Our approach uses the pre- extracted features and the features extracted through a deep mutil- level encoding network along with provided textual description to predict a

In the 2019 MediaEval task, various types of regression models were used to explore the use of image and video features for predicting media memorability.. Dos Santos and Almeida

In this approach we used human reaction to a second viewing of a video keyframe, to train a classifier for memorability, a true human- in-the-loop experiment.. The middle frame

From the returned evaluation results in Table. 1, the following conclusions can be observed: 1) The model using RNN and se- mantics is the best among all the five models. It

The results of the first two runs for the short-term subtask show that it is possible to achieve quite good results in VM prediction using models designed for IM prediction This

Our proposal relies on different learning strategies: for long-term memorability, we adopted k-NN regressors trained on hand-crafted motion features; and for short- term