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Author Dan Norton; Fernando Vilariño; Onur Ferhat edit  openurl
  Title Memory Field – Creative Engagement in Digital Collections Type Conference Article
  Year 2015 Publication Internet Librarian International Conference Abbreviated Journal  
  Volume (up) Issue Pages  
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  Abstract “Memory Fields” is a trans-disciplinary project aiming at the (re)valorisation of digital collections.Its main deliverable is an interface for a dual screen installation, used to access and mix the public library digital collections. The collections being used in this case are a collection of digitised posters from the Spanish Civil War, belonging to the Arxiu General de Catalunya, and a collection of field recordings made by Dan Norton. The system generates visualisations, and the images and sounds are mixed together using narrative primitives of video dj. Users contribute to the digital collections by adding personal memories and observations. The comments and recollections appear as flowers growing in a “memory field” and memories remain public in a Twitter feed (@Memoryfields).  
  Address London; UK; October 2015  
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  Area Expedition Conference ILI  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @NVF2015 Serial 2796  
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Author Fernando Vilariño; Dimosthenis Karatzas edit  openurl
  Title The Library Living Lab Type Conference Article
  Year 2015 Publication Open Living Lab Days Abbreviated Journal  
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  Address Istanbul; Turkey; August 2015  
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  Area Expedition Conference OLLD  
  Notes MV; DAG;SIAI Approved no  
  Call Number Admin @ si @ViK2015 Serial 2797  
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Author Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel edit  openurl
  Title An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres Type Book Chapter
  Year 2015 Publication The White Book on Public Library Network from Diputació de Barcelona Abbreviated Journal  
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  Notes MV; DAG;SIAI Approved no  
  Call Number Admin @ si @VKC2015 Serial 2798  
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Author Fernando Vilariño edit  openurl
  Title Computer Vision and Performing Arts Type Conference Article
  Year 2015 Publication Korean Scholars of Marketing Science Abbreviated Journal  
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  Address Seoul; Korea; October 2015  
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  Area Expedition Conference KAMS  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @Vil2015 Serial 2799  
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Author Fernando Vilariño; Dan Norton; Onur Ferhat edit  openurl
  Title Memory Fields: DJs in the Library Type Conference Article
  Year 2015 Publication 21 st Symposium of Electronic Arts Abbreviated Journal  
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  Address Vancouver; Canada; August 2015  
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  Area Expedition Conference ISEA  
  Notes ;SIAI Approved no  
  Call Number Admin @ si @VNF2015 Serial 2800  
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Author Fernando Vilariño; Dan Norton; Onur Ferhat edit  openurl
  Title The Eye Doesn't Click – Eyetracking and Digital Content Interaction Type Conference Article
  Year 2016 Publication 4S/EASST Conference Abbreviated Journal  
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  Address Barcelona; Spain; September 2016  
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  Area Expedition Conference EASST  
  Notes MV; 600.097;SIAI Approved no  
  Call Number Admin @ si @VNF2016 Serial 2801  
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Author Fernando Vilariño edit  openurl
  Title Giving Value to digital collections in the Public Library Type Conference Article
  Year 2016 Publication Librarian 2020 Abbreviated Journal  
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  Address Brussels; Belgium; October 2016  
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  Area Expedition Conference LIB  
  Notes MV; 600.097;SIAI Approved no  
  Call Number Admin @ si @Vil2016a Serial 2802  
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Author Fernando Vilariño; Dimosthenis Karatzas edit  openurl
  Title A Living Lab approach for Citizen Science in Libraries Type Conference Article
  Year 2016 Publication 1st International ECSA Conference Abbreviated Journal  
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  Address Berlin; Germany; May 2016  
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  Area Expedition Conference ECSA  
  Notes MV; DAG; 600.084; 600.097;SIAI Approved no  
  Call Number Admin @ si @ViK2016 Serial 2804  
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Author Fernando Vilariño edit  openurl
  Title Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation Type Conference Article
  Year 2016 Publication International Workshop on Poetry: Archives, Poetries and Receptions Abbreviated Journal  
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  Address Barcelona; Spain; October 2016  
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  ISSN ISBN Medium  
  Area Expedition Conference POETRY  
  Notes MV; 600.097;SIAI Approved no  
  Call Number Admin @ si @Vil2016b Serial 2805  
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Author Victor Ponce edit  url
openurl 
  Title Evolutionary Bags of Space-Time Features for Human Analysis Type Book Whole
  Year 2016 Publication PhD Thesis Universitat de Barcelona, UOC and CVC Abbreviated Journal  
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  Keywords Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture  
  Abstract The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice  
  Address June 2016  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Sergio Escalera;Xavier Baro;Hugo Jair Escalante  
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  Notes HuPBA Approved no  
  Call Number Pon2016 Serial 2814  
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Author Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo edit   pdf
doi  openurl
  Title Learning cross-spectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication 29th IEEE Conference on Computer Vision and Pattern Recognition Worshops Abbreviated Journal  
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  Abstract The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains.  
  Address Las vegas; USA; June 2016  
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  Area Expedition Conference CVPRW  
  Notes ADAS; 600.086; 600.076 Approved no  
  Call Number Admin @ si @AAS2016 Serial 2809  
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Author Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure edit   pdf
openurl 
  Title Slanted Stixels: Representing San Francisco's Steepest Streets} Type Conference Article
  Year 2017 Publication 28th British Machine Vision Conference Abbreviated Journal  
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  Abstract In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.  
  Address London; uk; September 2017  
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  Area Expedition Conference BMVC  
  Notes ADAS; 600.118 Approved no  
  Call Number ADAS @ adas @ HSE2017a Serial 2945  
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Author Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer edit   pdf
openurl 
  Title LIUM-CVC Submissions for WMT17 Multimodal Translation Task Type Conference Article
  Year 2017 Publication 2nd Conference on Machine Translation Abbreviated Journal  
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  Abstract This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.  
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  Area Expedition Conference WMT  
  Notes LAMP; 600.106; 600.120 Approved no  
  Call Number Admin @ si @ CAB2017 Serial 3035  
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Author Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville edit   pdf
url  openurl
  Title PixelVAE: A Latent Variable Model for Natural Images Type Conference Article
  Year 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal  
  Volume (up) Issue Pages  
  Keywords Deep Learning; Unsupervised Learning  
  Abstract Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms.  
  Address Toulon; France; April 2017  
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  Area Expedition Conference ICLR  
  Notes ADAS; 600.085; 600.076; 601.281; 600.118 Approved no  
  Call Number ADAS @ adas @ GKA2017 Serial 2815  
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Author Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera edit   pdf
openurl 
  Title ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results Type Conference Article
  Year 2016 Publication 14th European Conference on Computer Vision Workshops Abbreviated Journal  
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  Keywords Behavior Analysis; Personality Traits; First Impressions  
  Abstract This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge.
 
  Address Amsterdam; The Netherlands; October 2016  
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  Area Expedition Conference ECCVW  
  Notes HuPBA;MV; 600.063 Approved no  
  Call Number Admin @ si @ PCP2016 Serial 2828  
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