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Author | Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez | ||||
Title | Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem | Type | Journal | ||
Year | 2020 | Publication | Mathematics | Abbreviated Journal | MATH |
Volume | 20 | Issue | 8(9) | Pages | 1595 |
Keywords | STEM education; Project-based learning; Coding; software tool | ||||
Abstract | In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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Address | September 2020 | ||||
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Notes | IAM; ISE | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3722 | ||
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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 |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ ToG2020 | Serial | 3590 | ||
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Author | Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar | ||||
Title | Document Visual Question Answering Challenge 2020 | Type | Conference Article | ||
Year | 2020 | Publication | 33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper | Abbreviated Journal | |
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Abstract | This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template. | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ MTK2020 | Serial | 3558 | ||
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Author | Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas | ||||
Title | Text Recognition – Real World Data and Where to Find Them | Type | Conference Article | ||
Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 4489-4496 | ||
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Abstract | We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. | ||||
Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ JMG2020 | Serial | 3557 | ||
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Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
Title | Learning Graph Edit Distance by Graph NeuralNetworks | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. | ||||
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Notes | DAG; 600.121; 600.140; 601.302 | Approved | no | ||
Call Number | Admin @ si @ RFL2020 | Serial | 3555 | ||
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Author | Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
Title | Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images | Type | Journal Article | ||
Year | 2020 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
Volume | 10 | Issue | 22 | Pages | 8170 |
Keywords | sentiment analysis, personality trait analysis; weakly-supervised learning; visual classification; OCEAN model; social networks | ||||
Abstract | Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. | ||||
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Notes | ISE; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RVC2020b | Serial | 3553 | ||
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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 |
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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. |
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Notes | ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ RFB2020 | Serial | 3552 | ||
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Author | Idoia Ruiz; Joan Serrat | ||||
Title | Rank-based ordinal classification | Type | Conference Article | ||
Year | 2020 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 8069-8076 | ||
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Abstract | Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1. . . C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to least likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing
that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset. |
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Address | Virtual; January 2021 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RuS2020 | Serial | 3549 | ||
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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 | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ FRB2020b | Serial | 3547 | ||
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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 | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ FRB2020a | Serial | 3546 | ||
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Author | Mikel Menta; Adriana Romero; Joost Van de Weijer | ||||
Title | Learning to adapt class-specific features across domains for semantic segmentation | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:2001.08311
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image translation networks, which often contain a huge amount of domain-specific parameters. Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide sharp results. In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. To that aim, we design a conditional pixel-wise discriminator network, whose output is conditioned on the segmentation masks. Moreover, following recent advances in image translation, we adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network. Preliminary results on a segmentation task designed to assess the effectiveness of the proposed approach highlight the potential of the model, improving upon strong baselines and alternative designs. |
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MRW2020 | Serial | 3545 | ||
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Author | Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Simple and effective localized attribute representations for zero-shot learning | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:2006.05938
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps. |
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWH2020 | Serial | 3542 | ||
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Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz | ||||
Title | Unsupervised Domain Adaptation without Source Data by Casting a BAIT | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YWW2020 | Serial | 3539 | ||
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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 | |
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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. |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ TGM2020 | Serial | 3536 | ||
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Author | Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger | ||||
Title | Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | Type | Miscellaneous | ||
Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ CSS2020 | Serial | 3534 | ||
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