@mastersthesis {xNieto, title = {Video Saliency Prediction with Deep Neural Networks}, year = {2019}, abstract = {

Saliency prediction is a topic undergoing intense study in computer vision with a broad range of applications. It consists in predicting where the attention is going to be received in an image or a video by a human. Our work is based on a deep neural network named SalGAN, which was trained on a saliency annotated dataset of static images. In this thesis we investigate different approaches for extending SalGAN to the video domain. To this end, we investigate the recently proposed saliency annotated video dataset DHF1K to train and evaluate our models. The obtained results indicate that techniques such as depth estimation or coordconv can effectively be used as additional modalities to enhance the saliency prediction of static images obtained with SalGAN, achieving encouraging results in the DHF1K benchmark. Our work is based on pytorch and it is publicly available here.

Video Saliency Prediction with Deep Neural Networks - Juan Jose Nieto - DCU 2019 from Universitat Polit{\`e}cnica de Catalunya
}, author = {Nieto, Juan Jos{\'e}}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCalafell, title = {Video Retrieval of Specific Persons in Specific Locations}, year = {2016}, abstract = {

Student: Andrea Calafell

Advisors: Eva Mohedano (Insight), Kevin McGuinness (Insight), Noel E. O{\textquoteright}Connor (Insight) and Xavier Gir{\'o}-i-Nieto (UPC)

Program: Master in Computer Vision (Class of 2016)

Grade: A (9.0/10.0)

This thesis explores good practices for improving the detection of specific people in specific places. An approach combining recurrent and convolutional neural network have been considered to perform face detection. However, other more conventional methods have been tested, obtaining the best results by exploiting a deformable part model approach. A CNN is also used to obtain the face feature vectors and, \ with the purpose of helping in the face recognition, an approach to perform query expansion has been also developed. Furthermore, in order to be able to evaluate the different configurations in our non-labelled dataset, a user interface has been used to annotate the images and be able to obtain a precision of the system. Finally, different fusion and normalization strategies has been explored with the aim of combining the scores obtained from the face recognition with the ones obtained in the place recognition.

Video Retrieval of Specific Persons in Specific Locations from Xavier Giro
}, author = {Calafell, Andrea}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} }