Co-training for On-board Deep Object Detection

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e.objects) within the training images.Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this paper, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation

Surviving the Modernist Paradigm: a fresh approach to the singular art of Anglada-Camarasa, from Symbolism to Abstraction

This thesis deals with the Spanish artist Anglada-Camarasa (Barcelona, 1871- Palma de Mallorca, 1959) during the twenty years he lived in Paris: 1894-1914, when he enjoyed overwhelming international success. Until the 1980s, there was little institutional interest in his work and, hence, a dearth of literature on him. In my thesis I first offer an explanation of this state of affairs and then attempt a re-evaluation of his work. My explanation is articulated within the framework provided by the interpretation of early twentieth-century art history, originated in the 1970s, which emerged as an alternative to the dominating one defended by Modernist Paradigm supporters. In my discussion I situate Anglada's development within the cultural currents of his time and show how he found pictorial solutions to some of the artistic concerns of his contemporaries. Once the origins of the main features of Anglada's technique are firmly grasped, both in relation to subject matter and to pictorial means, it becomes much easier to understand his success, especially among his Russian admirers. Some of these, such as Meyerhold and Diaghilev, who were leading figures of the Russian cultural world and who were well known for their pioneering taste, found inspiration in Anglada's work for their innovations. Against the background of this historical and artistic analysis, I try to demonstrate that Anglada's figurative style influenced also Kandinsky's long transition into Abstraction, especially during the latter's stay in Murnau, before World War I, which constituted his most productive years. My overarching aim in carrying out this original investigation is to locate Anglada in the place he deserves in the beginning of the twentieth-century History of Art. By doing this, I hope not only to contribute to the still much-debated character of this period. But, more importantly, I hope to make Anglada better known, for the beauty of his work that expresses his faith in mankind potential which deserves to be given much closer attention

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our procedure shows improvements ranging from ~13 to ~26 mIoU points over baselines, so establishing new state-of-the-art results

Recognizing New Classes with Synthetic Data in the Loop : Application to Traffic Sign Recognition

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio for new/known classes; even for more challenging ratios such as , the results are also very positive

The UPV Design Factory. What is it good for?

Universities have the challenge and responsibility to society to train good professionals. Moreover, they must adapt to current demands. They must do so not only by improving the contents of the different degree programs but also by incorporating new programs and activities that help students develop soft skills, teamwork, connections between the university and real life, making them the best professionals and excellent citizens. To this end, in 2014, the UPV launched a program called Design Factory to channel and frame initiatives carried out by students to develop their prototypes and participate in student competitions. The program facilitates the creation of interdisciplinary learning communities in which students are committed to their goals, their teams, and the university. The program's spirit is to encourage learning in an eminently practical way. Students have to lead the projects, attract and select candidates, manage a budget, carry out their activities and try to achieve their goals, which involves many soft skills. For the program's operation, the university provides a team including management, technical and administrative staff, facilities, and economic endowment to the teams to carry out their activities. Funds are distributed in terms of the quality proposal, impact on the university, and results from the previous edition. More than 2,000 students participate in more than 60 engaged teams whose coordinators show high satisfaction with their roles in the current academic year

BIOTIC AND ABIOTIC EVENTS IN A SHALLOW CARBONATE PLATFORM (UPPER CRETACEOUS, SOUTH PYRENEAN MARGIN)

Πραγματοποιήθηκε μία λεπτoμεpής ιζηματολογική και παλαιοντολογική μελέτη των αποθέσεων ανθρακικής πλατφόρμας της περιοχής Serres Marginals (νότιο περιθώριο της Λεκάνης των Πυρρηναίων) και αναγνωρίσθηκαν τρεις ακολουθίες ρηχών ανθρακικών αποθέσεων (Cl, C2 και C3). Εντός των αποθέσεων ρηχής πλατφόρμας περιγράφονται ενδιάμεσης ενέργειας υποπαλιρροιακές έως ενδοπαλιρροιακές φάσεις, αβαθείς άμμοι υψηλής ενέργειας καθώς και λιμνοθαλάσσια έως λιμναία περιβάλλοντα.A detailed sedimentological and paleontological study of the Late Santonian-Late Campanian carbonate platform deposits of the Serres Marginals area (South margin of the Pyrenean Basin) has been performed. Three depositional shallow carbonate sequences have been distinguished (CI, C2 and C3). Within the shallow platform sequences moderate energy subtidal to intertidal, high energy sand-shoal, protected lagoon and restricted lagoon to lacustrine fades have been identified

Gold nanoparticles/silver-bipyridine hybrid nanobelts with tuned peroxidase-like activity

Gold nanoparticles-decorated silver-bipyridine coordination polymers with intrinsic peroxidase-like activity are reported. Both morphology and mimetic enzyme activity can be tuned by rational manipulation of the nanohybrid composition. The nanomaterial was used for the electrochemical determination of H2O2 and glucose

Effect on Health-related Quality of Life of changes in mental health in children and adolescents

<p>Abstract</p> <p>Background</p> <p>The objective of the study was to assess the effect of changes in mental health status on health-related quality of life (HRQOL) in children and adolescents aged 8 - 18 years.</p> <p>Methods</p> <p>A representative sample of Spanish children and adolescents aged 8-18 years completed the self-administered KIDSCREEN-52 questionnaire at baseline and after 3 years. Mental health status was measured using the Strengths and Difficulties Questionnaire (SDQ). Changes on SDQ scores over time were used to classify respondents in one of 3 categories (improved, stable, worsened). Data was also collected on gender, undesirable life events, and family socio-economic status. Changes in HRQOL were evaluated using effect sizes (ES). A multivariate analysis was performed to identify predictors of poor HRQOL at follow-up.</p> <p>Results</p> <p>Response rate at follow-up was 54% (n = 454). HRQOL deteriorated in all groups on most KIDSCREEN dimensions. Respondents who worsened on the SDQ showed the greatest deterioration, particularly on Psychological well-being (ES = -0.81). Factors most strongly associated with a decrease in HRQOL scores were undesirable life events and worsening SDQ score.</p> <p>Conclusions</p> <p>Changes in mental health status affect children and adolescents' HRQOL. Improvements in mental health status protect against poorer HRQOL while a worsening in mental health status is a risk factor for poorer HRQOL.</p