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Author Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez edit  openurl
  Title Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks Type Miscellaneous
  Year (down) 2018 Publication Arxiv Abbreviated Journal  
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  Abstract arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images.
In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts.
 
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  Notes ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ CRY2018 Serial 3550  
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Author Jon Almazan; Bojana Gajic; Naila Murray; Diane Larlus edit  doi
openurl 
  Title Re-ID done right: towards good practices for person re-identification Type Miscellaneous
  Year (down) 2018 Publication Arxiv Abbreviated Journal  
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  Abstract Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.  
  Address January 2018  
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  Notes Approved no  
  Call Number Admin @ si @ Serial 3711  
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Author Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven edit   pdf
openurl 
  Title End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Type Miscellaneous
  Year (down) 2017 Publication Arxiv Abbreviated Journal  
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  Abstract arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
 
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  Notes HuPBA; ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ GGM2017 Serial 2932  
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Author Fernando Vilariño edit  openurl
  Title Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation Type Miscellaneous
  Year (down) 2017 Publication Open Living Lab Days Report Abbreviated Journal  
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  Address Krakow; August 2017  
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  Notes MV; no menciona;SIAI Approved no  
  Call Number Admin @ si @ Vil2017b Serial 3033  
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Author Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez edit   pdf
openurl 
  Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
  Year (down) 2016 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Domain Adaptation; Pedestrian detection; Random Forest  
  Abstract Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVJ2016 Serial 2868  
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Author Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang edit  openurl
  Title An Effective Solution to Double Counting Problem in Human Pose Estimation Type Miscellaneous
  Year (down) 2015 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Pose estimation; double counting problem; mix-ture of parts Model  
  Abstract The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.

An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015].
 
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  Notes ISE; 600.078 Approved no  
  Call Number Admin @ si @ GHG2015 Serial 2590  
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit  openurl
  Title Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos Type Miscellaneous
  Year (down) 2015 Publication Arxiv Abbreviated Journal  
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  Abstract Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness.  
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  Notes MILAB Approved no  
  Call Number Admin @ si @ ADR2015b Serial 2713  
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Author Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda edit   pdf
openurl 
  Title ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos Type Miscellaneous
  Year (down) 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal  
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  Address Tarragona; juliol 2014  
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  Area Expedition Conference CIDUI  
  Notes IAM; ADAS; 600.076; 600.063; 600.075 Approved no  
  Call Number Admin @ si @ MGG2014 Serial 2457  
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil edit   pdf
openurl 
  Title Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type Miscellaneous
  Year (down) 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal  
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  Abstract  
  Address Tarragona; juliol 2014  
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  ISSN ISBN Medium  
  Area Expedition Conference CIDUI  
  Notes IAM; 600.075;DAG Approved no  
  Call Number Admin @ si @ SRM2014 Serial 2458  
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Author Adriana Romero; Petia Radeva; Carlo Gatta edit   pdf
openurl 
  Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
  Year (down) 2014 Publication Arxiv Abbreviated Journal  
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  Abstract CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
 
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  Notes MILAB; LAMP; 600.079 Approved no  
  Call Number Admin @ si @ RRG2014 Serial 2471  
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Author Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez edit  openurl
  Title 3d Pedestrian Detection via Random Forest Type Miscellaneous
  Year (down) 2014 Publication European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 231-238  
  Keywords Pedestrian Detection  
  Abstract Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications.
 
  Address Zurich; suiza; September 2014  
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  Area Expedition Conference ECCV-Demo  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ VRR2014 Serial 2570  
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Author Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate edit   pdf
openurl 
  Title Una experiencia de estructura, funcionamiento y evaluación de la asignatura de graficos por computador con metodologia de aprendizaje basado en proyectos Type Miscellaneous
  Year (down) 2013 Publication IV Congreso Internacional UNIVEST Abbreviated Journal  
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  Abstract IV Congreso Internacional UNIVEST  
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  Area Expedition Conference UNIVEST  
  Notes IAM; ADAS Approved no  
  Call Number Admin @ si @ MPG2013b Serial 2384  
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Author Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund edit   pdf
openurl 
  Title Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Type Miscellaneous
  Year (down) 2013 Publication ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream Abbreviated Journal  
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  Address Barcelona; October 2013  
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  Notes ISE Approved no  
  Call Number Admin @ si @ DDB2013 Serial 2372  
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Author Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil edit   pdf
openurl 
  Title Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos Type Miscellaneous
  Year (down) 2013 Publication IV Congreso Internacional UNIVEST Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Poster  
  Address Girona  
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  ISSN ISBN Medium  
  Area Expedition Conference UNIVEST  
  Notes IAM Approved no  
  Call Number Admin @ si @ MPG2013a Serial 2304  
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Author Laura Igual; Xavier Baro edit   pdf
openurl 
  Title Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación Type Miscellaneous
  Year (down) 2013 Publication Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación, de las XIX Jornadas sobre la Enseñanza Universitaria de la Informática Abbreviated Journal  
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  Area Expedition Conference JENUI  
  Notes OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ IgB2013 Serial 2257  
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