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Author Mikhail Mozerov; V. Kober; I.A. Ovseyevich edit  openurl
  Title A Stereo Matching Algorithm with Global Smoothness Criterion Type Miscellaneous
  Year 2006 Publication (down) Topical Meeting on Optoinformatics / Information Photonics, 133–135 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Saint-Petersburg (Russia)  
  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 Approved no  
  Call Number ISE @ ise @ MKO2006 Serial 675  
Permanent link to this record
 

 
Author Joan Serrat edit  openurl
  Title Aplicacion del analisis de imagenes en radiologia. Type Miscellaneous
  Year 1995 Publication (down) To be published in a CD––ROM edited by AERFAI, the Spanish image Analysis and Pattern. Abbreviated Journal  
  Volume Issue Pages  
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  Publisher Place of Publication Editor  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ Ser1995 Serial 120  
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Author Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui edit   pdf
url  openurl
  Title Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation Type Conference Article
  Year 2021 Publication (down) Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors.  
  Address Online; December 7-10, 2021  
  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 NIPS  
  Notes LAMP; 600.147; 600.141 Approved no  
  Call Number Admin @ si @ Serial 3691  
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Author Albin Soutif; Marc Masana; Joost Van de Weijer; Bartlomiej Twardowski edit   pdf
openurl 
  Title On the importance of cross-task features for class-incremental learning Type Conference Article
  Year 2021 Publication (down) Theory and Foundation of continual learning workshop of ICML Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform crosstask discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of crosstask features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small.  
  Address Virtual; July 2021  
  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 ICMLW  
  Notes LAMP Approved no  
  Call Number Admin @ si @ SMW2021 Serial 3588  
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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 (down) The White Book on Public Library Network from Diputació de Barcelona Abbreviated Journal  
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  Area Expedition Conference  
  Notes MV; DAG;SIAI Approved no  
  Call Number Admin @ si @VKC2015 Serial 2798  
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Author Angel Sappa; M.A. Garcia edit  openurl
  Title Generating compact representations of static scenes by means of 3D object hierarchies Type Journal
  Year 2007 Publication (down) The Visual Computer, 23(2): 143–154 Abbreviated Journal  
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  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 Approved no  
  Call Number ADAS @ adas @ SaG2007a Serial 798  
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Author Sergio Escalera; Marti Soler; Stephane Ayache; Umut Guçlu; Jun Wan; Meysam Madadi; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon edit  url
openurl 
  Title ChaLearn Looking at People: Inpainting and Denoising Challenges Type Book Chapter
  Year 2019 Publication (down) The Springer Series on Challenges in Machine Learning Abbreviated Journal  
  Volume Issue Pages 23-44  
  Keywords  
  Abstract Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined.  
  Address  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; no proj Approved no  
  Call Number Admin @ si @ ESA2019 Serial 3327  
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Author Sergio Escalera; Ralf Herbrich edit  url
doi  isbn
openurl 
  Title The NeurIPS’18 Competition: From Machine Learning to Intelligent Conversations Type Book Whole
  Year 2020 Publication (down) The Springer Series on Challenges in Machine Learning Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Sergio Escalera; Ralf Hebrick  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2520-1328 ISBN 978-3-030-29134-1 Medium  
  Area Expedition Conference  
  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ HeE2020 Serial 3328  
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Author Sergio Escalera; Stephane Ayache; Jun Wan; Meysam Madadi; Umut Guçlu; Xavier Baro edit  url
doi  openurl
  Title Inpainting and Denoising Challenges Type Book Whole
  Year 2019 Publication (down) The Springer Series on Challenges in Machine Learning Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration.
This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
 
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ EAW2019 Serial 3398  
Permanent link to this record
 

 
Author Hugo Jair Escalante; Sergio Escalera; Isabelle Guyon; Xavier Baro; Yagmur Gucluturk; Umut Guçlu; Marcel van Gerven edit  url
doi  openurl
  Title Explainable and Interpretable Models in Computer Vision and Machine Learning Type Book Whole
  Year 2018 Publication (down) The Springer Series on Challenges in Machine Learning Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.
This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:

·Evaluation and Generalization in Interpretable Machine Learning
·Explanation Methods in Deep Learning
·Learning Functional Causal Models with Generative Neural Networks
·Learning Interpreatable Rules for Multi-Label Classification
·Structuring Neural Networks for More Explainable Predictions
·Generating Post Hoc Rationales of Deep Visual Classification Decisions
·Ensembling Visual Explanations
·Explainable Deep Driving by Visualizing Causal Attention
·Interdisciplinary Perspective on Algorithmic Job Candidate Search
·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
·Inherent Explainability Pattern Theory-based Video Event Interpretations
 
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ EEG2018 Serial 3399  
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Author Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio edit  url
isbn  openurl
  Title Introduction to NIPS 2017 Competition Track Type Book Chapter
  Year 2018 Publication (down) The NIPS ’17 Competition: Building Intelligent Systems Abbreviated Journal  
  Volume Issue Pages 1-23  
  Keywords  
  Abstract Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?

In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Sergio Escalera; Markus Weimer  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-94042-7 Medium  
  Area Expedition Conference  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ EWB2018 Serial 3200  
Permanent link to this record
 

 
Author Oriol Ramos Terrades; Salvatore Tabbone; L. Wendling; Ernest Valveny edit  openurl
  Title Symbol Recognition based on a Multiresolution Analysis of the Radon Transform Type Miscellaneous
  Year 2004 Publication (down) The International Workshop on Multidisciplinary Image, Video, and Audio Retrieval and Mining Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Sherbrooke (Canada)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RTW2004 Serial 500  
Permanent link to this record
 

 
Author Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester edit   pdf
url  openurl
  Title Multilocal Creaseness Measure Type Journal
  Year 2012 Publication (down) The Insight Journal Abbreviated Journal IJ  
  Volume Issue Pages  
  Keywords Ridges, Valley, Creaseness, Structure Tensor, Skeleton,  
  Abstract This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.  
  Address  
  Corporate Author Alma IT Systems Thesis  
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  Language english Summary Language english Original Title  
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  Area Expedition Conference  
  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ VGL2012 Serial 1840  
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Author Angel Sappa; M.A. Garcia edit  openurl
  Title Incremental Integration of Multiresolution Range Images Type Journal
  Year 2007 Publication (down) The imaging science journal. Vol. 55, No. 3 pp. 127–139 Abbreviated Journal  
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  Address  
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  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SaG2007d Serial 812  
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre edit   pdf
isbn  openurl
  Title Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources Type Book Chapter
  Year 2016 Publication (down) The future of historical demography. Upside down and inside out Abbreviated Journal  
  Volume Issue Pages 127-131  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Acco Publishers Place of Publication Editor K.Matthijs; S.Hin; H.Matsuo; J.Kok  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-94-6292-722-3 Medium  
  Area Expedition Conference  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ PFL2016 Serial 2907  
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