Deep Learning and Uncertainty Modeling in Visual Food Analysis

[eng] The world of Machine Learning and Computer Vision has experienced a revolution since the last years. The appearance of Deep Learning algorithms and Convolutional Neural Networks, altogether with the increased processing capabilities provided by modern GPUs and the enormous amounts of annotated data publicly available, have allowed a boost in the field as never seen before. These notable improvements achieved in the Machine Learning world have led to the appearance of new fields like the Multimodal Learning, which encompasses and learns from many subfields. Additionally, new applications have taken profit of these advancements in order to reach high levels of performance. The huge results improvement of the currently available algorithms have allowed not only revolutionizing the academic world, but also bringing AI-based solutions to the market that looked like science fiction barely 10 years ago. This thesis, which is written as a papers compendium, focuses on delving deeper into the novel topic of Deep Multimodal Learning by proposing new algorithms and solutions for both already existing and newly defined problems. From the applications perspective, most of the papers presented can be divided in two areas of applicability. From the one hand, Egocentric Vision and Storytelling, which consists in acquiring images from the daily life of a person in order to analyse its behaviour patterns like social interactions, activities and events, interactions with objects, etc. And on the other hand, Food Recognition and Analysis, which consists in visually analysing and recognizing the food appearing on images in multiple contexts and with different levels of complexity, from food groups recognition to nutritional analysis. In both applications, the final purpose of the proposed papers is building tools that provide information that could lead to a better quality of life of the users. [spa] El mundo del Machine Learning y la Visión por Computador ha experimentado una revolución los últimos años. La aparición de algoritmos de Deep Learning y Convolutional Neural Networks, junto con las mayores capacidades de procesamiento proporcionadas por GPU modernas y las enormes cantidades de datos anotados disponibles públicamente, han permitió un impulso en el campo como nunca antes se había visto.Estas notables mejoras logradas en el mundo del Machine Learning han llevado a la aparición de nuevos campos como el Aprendizaje Multimodal, que engloba y aprende de muchos subc