@mastersthesis {xBernal, title = {Predicting emotion in movies: Recurrent and convolutional models applied to videos}, year = {2017}, abstract = {

This Thesis explores di erent approaches using deep learning techniques to predict emotions\ in videos.\ Working with videos implies a huge amount of data including visual frames and acoustic\ samples. The rst step of the project is basically to extract features to represent the videos in\ small sets of arrays. This procedure is done using pre-trained models based on Convolutional\ Networks, the state of the art in visual recognition. Firstly, visual features are extracted using\ 3D convolutions and acoustic features are extracted using VGG19, a pre-trained convolutional\ model for images fi ne-tuned to accept the audio inputs.\ Later, these features are fed into a Recurrent model capable of exploiting the temporal information.\ Emotions are measured in terms of valence and arousal, values between [-1, 1]. Additionally,\ the same techniques are also used to attempt to predict fear scenes. In consequence, this thesis\ deals with both regression and classi cation problems.\ Several architectures and di erent parameters have been tested in order to achieve the best\ performance. Finally, the results will be published in the MediaEval 2017 Challenge and compared\ to the state-of-the-art solutions.

Predicting Emotion in Movies from Xavier Giro-i-Nieto
}, author = {Bernal, Oriol}, editor = {Xavier Gir{\'o}-i-Nieto and Zaharieva, Maia} } @mastersthesis {xCardoner, title = {Predicting Media Interestingness}, year = {2017}, abstract = {

This thesis explores the application of a deep learning approach for the prediction of media\ interestingness. Two di erent models are investigated, one for the prediction of image and one\ for the prediction of video interestingness.\ For the prediction of image interestingness, the ResNet50 network is ne-tuned to obtain\ best results. First, some layers are added. Next, the model is trained and fine-tuned using data\ augmentation, dropout, class weights, and changing other hyper parameters.\ For the prediction of video interestingness, rst, features are extracted with a 3D convolutional\ network. Next a LSTM network is trained and fine-tuned with the features.\ The nal result is a binary label for each image/video: 1 for interesting, 0 for not interesting.\ Additionally, a con fidence value is provided for each prediction. Finally, the Mean Average\ Precision (MAP) is employed as evaluation metric to estimate the quality of the fi nal results.

Predicting Media Interestingness from Xavier Giro-i-Nieto
}, author = {Cardoner, LLuc}, editor = {Zaharieva, Maia and Xavier Gir{\'o}-i-Nieto} }