@mastersthesis {xAssens, title = {The Temporal Dimension of Visual Attention Models}, year = {2017}, abstract = {

Program: Bachelor Degree on Telecommunications Science and Technologies (CITTEL)

Grade: A with honours (10.0/10.0)

This thesis explores methodologies for scanpath prediction on images using deep learning\ frameworks.\ As a preliminary step, we analyze the characteristics of the data provided by di erent datasets.\ We then explore the use of Convolutional Neural Networks (CNN) and Long-Short-Term-Memory\ (LSTM) newtworks for scanpath prediction. We observe that these models fail due to the high\ stochastic nature of the data.\ With the gained insight, we propose a novel time-aware visual saliency representation named\ Saliency Volume, that averages scanpaths over multiple observers.\ Next, we explore the SalNet network and adapt it for saliency volume prediction, and we find\ several ways of generating scanpaths from saliency volumes.\ Finally, we ne-tuned our model for scanpaht prediction on 360-degree images and successfully\ submitted it to the Salient360! Challenge from ICME. The source code and models are publicly\ available at https://github.com/massens/saliency-360salient-2017.

The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, author = {Assens, Marc}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and Noel E. O{\textquoteright}Connor} } @mastersthesis {xReyesa, title = {Time-sensitive Egocentric Image Retrieval for Fidings Objects in Lifelogs}, year = {2016}, abstract = {

Advisors: Eva Mohedano (Insight DCU), Kevin McGuinness (Insight DCU) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9.4/10.0)

This work explores diverse practices for conducting an object search from large amounts of egocentric images taking into account their temporal information. The application of this technology is to identify where personal belongings were lost or forgotten. We develop a pipeline-structured system. Firstly, the images of the day being scanned are sorted based on their probability to depict the forgotten object. This stage is solved by applying an existing visual search engine based on deep learning features. Secondly, a learned threshold selects the top ranked images as candidates to contain the object. Finally the images are reranked based on temporal and diversity criteria. Furthermore, we build a validation environment for assessing the system{\textquoteright}s performance aiming to find the optimal configuration of its parameters. Due to the lack of related works to be compared with, this thesis proposes an novel evaluation framework and metric to assess the problem.

[Tfg cristian reyes] time sensitive egocentric image retrieval for finding objects in lifelogs from Xavier Giro
}, author = {Reyes, Cristian}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} }