(7.03 MB) Abstract
Advisors: Santiago de la Puente and Xavier Giró-i-Nieto
Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at Telecom BCN-ETSETB from the Technical University of Catalonia (UPC)
Grade: A with honors (10/10.0)
This thesis studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62\% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations. The source code and models are publicly available at https://github.com/imatge-upc/vqa-2016-cvprw.
- Download the report from UPCommons or arXiv.
- Project page on GitHub.
