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	Author	Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Joaquim Punti Vidal; Pilar Medina Bravo; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez
	Title	Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis			Type	Journal Article
	Year	2020	Publication	Journal of Medical Internet Research	Abbreviated Journal	JMIR
	Volume	22	Issue	7	Pages	e17758
	Keywords
	Abstract	Background: Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective: This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods: We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results: We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions: The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ RFB2020			Serial	3552
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	Author	Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski
	Title	Kornia: an Open Source Differentiable Computer Vision Library for PyTorch			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aspen; Colorado; USA; March 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	WACV
	Notes	MSIAU; 600.122; 600.130			Approved	no
	Call Number	Admin @ si @ RMP2020			Serial	3291
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	Author	Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva
	Title	Uncertainty Modeling and Deep Learning Applied to Food Image Analysis			Type	Conference Article
	Year	2020	Publication	13th International Joint Conference on Biomedical Engineering Systems and Technologies	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis.
	Address	Villetta; Malta; February 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	BIODEVICES
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ANK2020			Serial	3526
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	Author	Eduardo Aguilar; Petia Radeva
	Title	Uncertainty-aware integration of local and flat classifiers for food recognition			Type	Journal Article
	Year	2020	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	136	Issue		Pages	237-243
	Keywords
	Abstract	Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2020			Serial	3525
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	Author	Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva
	Title	Eating Habits Discovery in Egocentric Photo-streams			Type	Miscellaneous
	Year	2020	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	CoRR abs/2009.07646 Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ TGM2020			Serial	3536
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	Author	Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva
	Title	Topic modelling for routine discovery from egocentric photo-streams			Type	Journal Article
	Year	2020	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	104	Issue		Pages	107330
	Keywords	Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling
	Abstract	Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TWP2020			Serial	3435
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	Author	Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva
	Title	Hierarchical approach to classify food scenes in egocentric photo-streams			Type	Journal Article
	Year	2020	Publication	IEEE Journal of Biomedical and Health Informatics	Abbreviated Journal	J-BHI
	Volume	24	Issue	3	Pages	866 - 877
	Keywords
	Abstract	Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TLM2020			Serial	3380
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	Author	Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov
	Title	Variable Rate Deep Image Compression with Modulated Autoencoder			Type	Journal Article
	Year	2020	Publication	IEEE Signal Processing Letters	Abbreviated Journal	SPL
	Volume	27	Issue		Pages	331-335
	Keywords
	Abstract	Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; ADAS; 600.141; 600.120; 600.118			Approved	no
	Call Number	Admin @ si @ YHW2020			Serial	3346
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	Author	Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov
	Title	Improved Discrete Optical Flow Estimation With Triple Image Matching Cost			Type	Journal Article
	Year	2020	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	8	Issue		Pages	17093 - 17102
	Keywords
	Abstract	Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ YCW2020			Serial	3345
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	Author	Fernando Vilariño
	Title	Unveiling the Social Impact of AI			Type	Conference Article
	Year	2020	Publication	Workshop at Digital Living Lab Days Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	September 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MV; DAG; 600.121; 600.140;SIAI			Approved	no
	Call Number	Admin @ si @ Vil2020			Serial	3459
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	Author	Gabriel Villalonga; Antonio Lopez
	Title	Co-Training for On-Board Deep Object Detection			Type	Journal Article
	Year	2020	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume		Issue		Pages	194441 - 194456
	Keywords
	Abstract	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 article, 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.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ ViL2020			Serial	3488
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	Author	Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez
	Title	Recognizing new classes with synthetic data in the loop: application to traffic sign recognition			Type	Journal Article
	Year	2020	Publication	Sensors	Abbreviated Journal	SENS
	Volume	20	Issue	3	Pages	583
	Keywords
	Abstract	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∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; ADAS; 600.118; 600.120			Approved	no
	Call Number	Admin @ si @ VWL2020			Serial	3405
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	Author	Giovanni Maria Farinella; Petia Radeva; Jose Braz
	Title	Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications			Type	Book Whole
	Year	2020	Publication	Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020	Abbreviated Journal
	Volume	4	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ FRB2020a			Serial	3546
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	Author	Giovanni Maria Farinella; Petia Radeva; Jose Braz
	Title	Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications			Type	Book Whole
	Year	2020	Publication	Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020	Abbreviated Journal
	Volume	5	Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ FRB2020b			Serial	3547
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	Author	Guillermo Torres; Debora Gil
	Title	A multi-shape loss function with adaptive class balancing for the segmentation of lung structures			Type	Journal Article
	Year	2020	Publication	International Journal of Computer Assisted Radiology and Surgery	Abbreviated Journal	IJCAR
	Volume	15	Issue	1	Pages	S154-55
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM			Approved	no
	Call Number	Admin @ si @ ToG2020			Serial	3590
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