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| Author | Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer | ||||
| Title | Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. | Type | Conference Article | ||
| Year | 2020 | Publication | International Workshop on Artificial Intelligence for Healthcare Applications | Abbreviated Journal  |
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| Volume | 12661 | Issue | Pages | 476-489 | |
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| Abstract | Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field. | ||||
| Address | Virtual; January 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | DAG; 600.121; 600.140; | Approved | no | ||
| Call Number | Admin @ si @ BCF2020 | Serial | 3508 | ||
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| Author | Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
| Title | Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents | Type | Conference Article | ||
| Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal |
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| Abstract | The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from such documents in a robust and efficient way. In addition, the semi-structured nature of these reports is specially suited for the use of graph-based representations which are flexible enough to adapt to the deformations from the different document templates. Moreover, Graph Neural Networks provide the proper methodology to learn relations among the data elements in these documents. In this work we study the use of Graph Neural Network architectures to tackle the problem of entity recognition and relation extraction in semi-structured documents. Our approach achieves state of the art results in the three tasks involved in the process. Additionally, the experimentation with two datasets of different nature demonstrates the good generalization ability of our approach. |
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| Address | Virtual; January 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CRV2020 | Serial | 3509 | ||
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| Author | M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Learning to Rank for Active Learning: A Listwise Approach | Type | Conference Article | ||
| Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal |
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| Volume | Issue | Pages | 5587-5594 | ||
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| Abstract | Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks. | ||||
| Address | Virtual; January 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LLW2020a | Serial | 3511 | ||
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| Author | Gemma Rotger | ||||
| Title | Lifelike Humans: Detailed Reconstruction of Expressive Human Faces | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal |
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| Abstract | Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. | ||||
| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Felipe Lumbreras;Antonio Agudo | |
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | 978-84-122714-3-0 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Rot2021 | Serial | 3513 | ||
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| Author | Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez | ||||
| Title | Explainable Early Stopping for Action Unit Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | Faces and Gestures in E-health and welfare workshop | Abbreviated Journal |
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| Volume | Issue | Pages | 693-699 | ||
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| Abstract | A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it. In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it, we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FGW | ||
| Notes | HUPBA; | Approved | no | ||
| Call Number | Admin @ si @ CME2020 | Serial | 3514 | ||
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| Author | Anna Esposito; Terry Amorese; Nelson Maldonato; Alessandro Vinciarelli; Maria Ines Torres; Sergio Escalera; Gennaro Cordasco | ||||
| Title | Seniors’ ability to decode differently aged facial emotional expressions | Type | Conference Article | ||
| Year | 2020 | Publication | Faces and Gestures in E-health and welfare workshop | Abbreviated Journal |
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| Volume | Issue | Pages | 716-722 | ||
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| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FGW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ EAM2020 | Serial | 3515 | ||
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| Author | Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis | ||||
| Title | Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents | Type | Conference Article | ||
| Year | 2020 | Publication | Faces and Gestures in E-health and welfare workshop | Abbreviated Journal |
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| Volume | Issue | Pages | 667-674 | ||
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| Address | Virtual; November 2020 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | FGW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ ECE2020 | Serial | 3516 | ||
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| Author | Josep Famadas; Meysam Madadi; Cristina Palmero; Sergio Escalera | ||||
| Title | Generative Video Face Reenactment by AUs and Gaze Regularization | Type | Conference Article | ||
| Year | 2020 | Publication | 15th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal |
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| Volume | Issue | Pages | 444-451 | ||
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| Abstract | In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization. | ||||
| Address | Virtual; November 2020 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ FMP2020 | Serial | 3517 | ||
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| Author | Carlos Martin-Isla; Maryam Asadi-Aghbolaghi; Polyxeni Gkontra; Victor M. Campello; Sergio Escalera; Karim Lekadir | ||||
| Title | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial Pathology Segmentation challenge | Type | Conference Article | ||
| Year | 2020 | Publication | MYOPS challenge and workshop | Abbreviated Journal |
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| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | MICCAIW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ MAG2020 | Serial | 3518 | ||
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| Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
| Title | CLOTH3D: Clothed 3D Humans | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal |
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| Abstract | This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ BME2020 | Serial | 3519 | ||
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| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
| Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal |
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| Address | Virtual; August 2020 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AAF2020 | Serial | 3520 | ||
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| Author | Petia Radeva | ||||
| Title | Uncertainty Modeling within an End-to-end Framework for Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 1st DELTA | Abbreviated Journal |
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| Area | Expedition | Conference | DELTA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
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| Author | Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva | ||||
| Title | Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal |
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| Volume | 12538 | Issue | Pages | 469-484 | |
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| Abstract | The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. | ||||
| Address | Virtual; August 2020 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ MTM2020 | Serial | 3528 | ||
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| Author | Mariona Caros; Maite Garolera; Petia Radeva; Xavier Giro | ||||
| Title | Automatic Reminiscence Therapy for Dementia | Type | Conference Article | ||
| Year | 2020 | Publication | 10th ACM International Conference on Multimedia Retrieval | Abbreviated Journal |
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| Volume | Issue | Pages | 383-387 | ||
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| Abstract | With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. | ||||
| Address | Virtual; October 2020 | ||||
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| Area | Expedition | Conference | ICRM | ||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ CGR2020 | Serial | 3529 | ||
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| Author | Esmitt Ramirez; Carles Sanchez; Debora Gil | ||||
| Title | Localizing Pulmonary Lesions Using Fuzzy Deep Learning | Type | Conference Article | ||
| Year | 2019 | Publication | 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing | Abbreviated Journal |
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| Volume | Issue | Pages | 290-294 | ||
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| Abstract | The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions. | ||||
| Address | Timisoara; Rumania; September 2019 | ||||
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| Area | Expedition | Conference | SYNASC | ||
| Notes | IAM; 600.145; 600.140; 601.337; 601.323 | Approved | no | ||
| Call Number | Admin @ si @ RSG2019 | Serial | 3531 | ||
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