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Author (up) David Masip; Jordi Vitria
Title Classifier Combination Applied to Real Time Face Detection and Classification. Type Book Chapter
Year 2004 Publication Recerca Automatica, Visio i Robotica, Ed. UPC, A. Grau, V. Puig (Eds.), 345–353, ISBN 84–7653–844–8 Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MBV2004b Serial 449
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Author (up) David Masip; Jordi Vitria
Title Boosted Linear Projections for Discriminant Analysis Type Miscellaneous
Year 2004 Publication CCIA 2004, 45–52, ISBN: 1–58603–466–9 Abbreviated Journal
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Address IOS Press
Corporate Author Thesis
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2004c Serial 510
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Author (up) David Masip; Jordi Vitria
Title Feature Extraction for Nearest Neighbor Classification. Application to Gender Recognition Type Journal
Year 2005 Publication International Journal of Intelligent Systems, 20(5): 561–576 (IF: 0.657) Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2005 Serial 562
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Author (up) David Masip; Jordi Vitria
Title Boosted discriminant projections for nearest neighbor classification Type Journal
Year 2006 Publication Pattern Recognition, 39(2): 164–170 Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2006 Serial 634
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Author (up) David Masip; Jordi Vitria
Title Shared Feature Extraction for Nearest Neighbor Face Recognition Type Journal
Year 2008 Publication IEEE Transactions on Neural Networks Abbreviated Journal
Volume 19 Issue 4 Pages 586–595
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Area Expedition Conference
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2008 Serial 944
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Author (up) David Masip; Ludmila I. Kuncheva; Jordi Vitria
Title An ensemble-based method for linear feature extraction for two-class problems Type Journal
Year 2005 Publication Pattern Analysis and Applications, 8(3): 227–237 (IF: 0.782) Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MKV2005 Serial 613
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Author (up) David Masip; M. Bressan; Jordi Vitria
Title Classifier Combination Applied to Real Time Face Detection and Classification Type Miscellaneous
Year 2004 Publication AVR2004 Abbreviated Journal
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Abstract
Address Barcelona
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 OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MBV2004a Serial 448
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Author (up) David Masip; M. Bressan; Jordi Vitria
Title Feature extraction methods for real-time face detection and classification Type Journal
Year 2005 Publication Eurasip Journal on Applied Signal Processing, 13: 2061–2071 Abbreviated Journal
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MBV2005 Serial 612
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Author (up) David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson
Title Automated Prediction of Preferences Using Facial Expressions Type Journal Article
Year 2014 Publication PloS one Abbreviated Journal Plos
Volume 9 Issue 2 Pages e87434
Keywords
Abstract We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.
Address
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Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ MNT2014 Serial 2453
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Author (up) David Pujol Perich; Albert Clapes; Sergio Escalera
Title SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
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Notes HUPBA Approved no
Call Number Admin @ si @ PCE2023 Serial 4014
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Author (up) David Roche
Title A Statistical Framework for Terminating Evolutionary Algorithms at their Steady State Type Book Whole
Year 2015 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract As any iterative technique, it is a necessary condition a stop criterion for terminating Evolutionary Algorithms (EA). In the case of optimization methods, the algorithm should stop at the time it has reached a steady state so it can not improve results anymore. Assessing the reliability of termination conditions for EAs is of prime importance. A wrong or weak stop criterion can negatively a ect both the computational e ort and the nal result.
In this Thesis, we introduce a statistical framework for assessing whether a termination condition is able to stop EA at its steady state. In one hand a numeric approximation to steady states to detect the point in which EA population has lost its diversity has been presented for EA termination. This approximation has been applied to di erent EA paradigms based on diversity and a selection of functions covering the properties most relevant for EA convergence. Experiments show that our condition works regardless of the search space dimension and function landscape and Di erential Evolution (DE) arises as the best paradigm. On the other hand, we use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in xspace.
Our theoretical framework is analyzed across several benchmark test functions
and two standard termination criteria based on function improvement in f-space and EA population x-space distribution for the DE paradigm. Results validate our statistical framework as a powerful tool for determining the capability of a measure for terminating EA and select the x-space distribution as the best-suited for accurately stopping DE in real-world applications.
Address July 2015
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Debora Gil;Jesus Giraldo
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ Roc2015 Serial 2686
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Author (up) David Roche; Debora Gil; Jesus Giraldo
Title Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms Type Conference Article
Year 2011 Publication 11th European Conference on Artificial Life Abbreviated Journal
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Abstract A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.
Address Paris, France
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 ECAL
Notes IAM; Approved no
Call Number IAM @ iam @ RGG2011b Serial 1678
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Author (up) David Roche; Debora Gil; Jesus Giraldo
Title An inference model for analyzing termination conditions of Evolutionary Algorithms Type Conference Article
Year 2011 Publication 14th Congrès Català en Intel·ligencia Artificial Abbreviated Journal
Volume Issue Pages 216-225
Keywords Evolutionary Computation Convergence, Termination Conditions, Statistical Inference
Abstract In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution.
Address Lleida, Catalonia (Spain)
Corporate Author Associació Catalana Intel·ligència Artificial 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 978-1-60750-841-0 Medium
Area Expedition Conference CCIA
Notes IAM Approved no
Call Number IAM @ iam @ RGG2011a Serial 1677
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Author (up) David Roche; Debora Gil; Jesus Giraldo
Title Assessing agonist efficacy in an uncertain Em world Type Conference Article
Year 2012 Publication 40th Keystone Symposia on mollecular and celular biology Abbreviated Journal
Volume Issue Pages 79
Keywords
Abstract The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed.
Address Fairmont Banff Springs, Banff, Alberta, Canada
Corporate Author Keystone Symposia Thesis
Publisher Keystone Symposia Place of Publication Editor A. Christopoulus and M. Bouvier
Language english Summary Language english Original Title
Series Editor Keystone Symposia Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference KSMCB
Notes IAM Approved no
Call Number IAM @ iam @ RGG2012 Serial 1855
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Author (up) David Roche; Debora Gil; Jesus Giraldo
Title Detecting loss of diversity for an efficient termination of EAs Type Conference Article
Year 2013 Publication 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages 561 - 566
Keywords EA termination; EA population diversity; EA steady state
Abstract Termination of Evolutionary Algorithms (EA) at its steady state so that useless iterations are not performed is a main point for its efficient application to black-box problems. Many EA algorithms evolve while there is still diversity in their population and, thus, they could be terminated by analyzing the behavior some measures of EA population diversity. This paper presents a numeric approximation to steady states that can be used to detect the moment EA population has lost its diversity for EA termination. Our condition has been applied to 3 EA paradigms based on diversity and a selection of functions
covering the properties most relevant for EA convergence.
Experiments show that our condition works regardless of the search space dimension and function landscape.
Address Timisoara; Rumania;
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 978-1-4799-3035-7 Medium
Area Expedition Conference SYNASC
Notes IAM; 600.044; 600.060; 605.203 Approved no
Call Number Admin @ si @ RGG2013c Serial 2299
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