@mastersthesis {xPana, title = {Visual Saliency Prediction using Deep learning Techniques}, year = {2015}, abstract = {

Advisor: 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 with honors (9.9/10.0)

A saliency map is a model that predicts eye fixations on a visual scene. In other words, it is the prediction of saliency areas in images has been traditionally addressed with hand crafted features inspired on neuroscience principles. This work however addresses the problem with a completely data-driven approach by training a convolutional network. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep network architecture which is both fast and accurate. In our system, named JuntingNet, the learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The convolutional network developed in this work, named JuntingNet, won the CVPR Large-scale Scene UNderstanding (LSUN) 2015 challenge on saliency prediction with a superior performance in all considered metrics.

Saliency prediction using deep learning techniques from Xavier Giro

2015-TFG-JuntingPan-VisualSaliencyPredictionUsingDeepLearningTechniques from Image Processing Group on Vimeo.

See https://imatge.upc.edu/web/resources/end-end-convolutional-networks-saliency-prediction-software.

}, author = {Pan, Junting}, editor = {Xavier Gir{\'o}-i-Nieto} }