Transfer Learning for Video Memorability Prediction

Index Terms— Video interestingness prediction, social interestingness, content interestingness, multimodal fusion, deep neural network (DNN), MediaEval

In land mobile radio commullications, the propagation 105s prediction involves the evaluations of rrcc space 1055, reflection loss from the earLh surface, diffraction tOllS

hence, we can conclude that the proposed method results in well-calibrated PIs not only on the training set but also on the validation set. 7, the testing solutions

Deep learning architectures which have been recently proposed for the pre- diction of salient areas in images differ essentially by the quantity of convolution and pooling layers,

A state-of-the-art statistical prediction model is used for giving two-day ahead estimations of the wind farm hourly power generation, with Hirlam Numerical Weather Predictions

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 this study, LSTM (Long-Short Term Memory) neural network models are constructed to predict travel time for both long term and short term using real world data of New York

and high level visual features, deep learning based action recogni- tion representations (C3D-Preds), and image and video captions for memorability prediction.. The major findings