Home [1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–30]
List View
 | 
Citations
 | 
Details
   web
Records
Author Dustin Carrion Ojeda; Hong Chen; Adrian El Baz; Sergio Escalera; Chaoyu Guan; Isabelle Guyon; Ihsan Ullah; Xin Wang; Wenwu Zhu
Title NeurIPS’22 Cross-Domain MetaDL competition: Design and baseline results Type Conference Article
Year 2022 Publication Understanding Social Behavior in Dyadic and Small Group Interactions Abbreviated Journal
Volume 191 Issue Pages 24-37
Keywords
Abstract We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on “cross-domain” meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve “any-way” and “any-shot” problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of “ways” (within the range 2-20) and any number of “shots” (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains.
Address
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 (down) PMLR
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ CCB2022 Serial 3802
Permanent link to this record
 
 
Author Francesco Ciompi; A. Palaioroutas; M. Loeve; Oriol Pujol; Petia Radeva; H. Tiddens; M. de Bruijne
Title Lung Tissue Classification in Severe Advanced Cystic Fibrosis from CT Scans Type Conference Article
Year 2011 Publication In MICCAI 2011 4th International Workshop on Pulmonary Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Toronto, Canada
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 (down) PIA
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPL2011 Serial 1798
Permanent link to this record
 
 
Author S.Grau; Ana Puig; Sergio Escalera; Maria Salamo
Title Intelligent Interactive Volume Classification Type Conference Article
Year 2013 Publication Pacific Graphics Abbreviated Journal
Volume 32 Issue 7 Pages 23-28
Keywords
Abstract This paper defines an intelligent and interactive framework to classify multiple regions of interest from the original data on demand, without requiring any preprocessing or previous segmentation. The proposed intelligent and interactive approach is divided in three stages: visualize, training and testing. First, users visualize and label some samples directly on slices of the volume. Training and testing are based on a framework of Error Correcting Output Codes and Adaboost classifiers that learn to classify each region the user has painted. Later, at the testing stage, each classifier is directly applied on the rest of samples and combined to perform multi-class labeling, being used in the final rendering. We also parallelized the training stage using a GPU-based implementation for
obtaining a rapid interaction and classification.
Address
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 978-3-905674-50-7 Medium
Area Expedition Conference (down) PG
Notes HuPBA; 600.046;MILAB Approved no
Call Number Admin @ si @ GPE2013b Serial 2355
Permanent link to this record
 
 
Author David Vazquez; Antonio Lopez
Title Intrusion Classification in Intelligent Video Surveillance Systems Type Report
Year 2008 Publication Estudis d'Enginyeria Superior en Informática Abbreviated Journal UAB
Volume Issue Pages
Keywords Human detection; Car detection; Intrusion detection
Abstract An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system
Address Bellaterra, Spain
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 (down) PFC
Notes ADAS Approved no
Call Number ADAS @ adas @ VL2008a Serial 1670
Permanent link to this record
 
 
Author Maria Ines Torres; Javier Mikel Olaso; Cesar Montenegro; Riberto Santana; A.Vazquez; Raquel Justo; J.A.Lozano; Stephan Schogl; Gerard Chollet; Nazim Dugan; M.Irvine; N.Glackin; C.Pickard; Anna Esposito; Gennaro Cordasco; Alda Troncone; Dijana Petrovska Delacretaz; Aymen Mtibaa; Mohamed Amine Hmani; M.S.Korsnes; L.J.Martinussen; Sergio Escalera; C.Palmero Cantariño; Olivier Deroo; O.Gordeeva; Jofre Tenorio Laranga; E.Gonzalez Fraile; Begoña Fernandez Ruanova; A.Gonzalez Pinto
Title The EMPATHIC project: mid-term achievements Type Conference Article
Year 2019 Publication 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments Abbreviated Journal
Volume Issue Pages 629-638
Keywords
Abstract Maria Ines Torres; Javier Mikel Olaso, César Montenegro, Riberto Santana, A. Vázquez, Raquel Justo, J. A. Lozano, Stephan Schlögl, Gérard Chollet, Nazim Dugan, M. Irvine, N. Glackin, C. Pickard, Anna Esposito, Gennaro Cordasco, Alda Troncone, Dijana Petrovska-Delacrétaz, Aymen Mtibaa, Mohamed Amine Hmani, M. S. Korsnes, L. J. Martinussen, Sergio Escalera, C. Palmero Cantariño, Olivier Deroo, O. Gordeeva, Jofre Tenorio-Laranga, E. Gonzalez-Fraile, Begoña Fernández-Ruanova, A. Gonzalez-Pinto
Address Rhodes Greece; June 2019
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 (down) PETRA
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ TOM2019 Serial 3325
Permanent link to this record
 
 
Author Wenjuan Gong; Jürgen Brauer; Michael Arens; Jordi Gonzalez
Title Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation Type Conference Article
Year 2011 Publication 1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address London, United Kingdom
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 (down) PERHAPS
Notes ISE Approved no
Call Number Admin @ si @ GBA2011 Serial 1812
Permanent link to this record
 
 
Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title User Verification From Walking Activity. First Steps Towards a Personal Verification System Type Conference Article
Year 2011 Publication 1st International Conference on Pervasive and Embedded Computing and Communication Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Algarve, Portugal
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 (down) PECCS
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011c Serial 1762
Permanent link to this record
 
 
Author Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo
Title RGBN Multispectral Images: a Novel Color Restoration Approach Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal
Volume Issue Pages
Keywords Multispectral Imaging; Free Sensor Model; Neural Network
Abstract This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided.
Address Porto; Portugal; June 2017
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 (down) PAAMS
Notes ADAS; MSIAU; 600.118; 600.122 Approved no
Call Number Admin @ si @ ASS2017 Serial 2918
Permanent link to this record
 
 
Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Learning to Colorize Infrared Images Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal
Volume Issue Pages
Keywords CNN in multispectral imaging; Image colorization
Abstract This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
Address Porto; Portugal; June 2017
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 (down) PAAMS
Notes ADAS; MSIAU; 600.086; 600.122; 600.118 Approved no
Call Number Admin @ si @ Serial 2919
Permanent link to this record
 
 
Author Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez
Title Robust Detection of Outdoor Urban Advertising Panels in Static Images Type Conference Article
Year 2019 Publication 18th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal
Volume Issue Pages 246-256
Keywords Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality
Abstract One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images.
Address Aquila; Italia; June 2019
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 (down) PAAMS
Notes MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ MSS2019 Serial 3270
Permanent link to this record
 
 
Author Youssef El Rhabi; Simon Loic; Brun Luc
Title Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel Type Conference Article
Year 2015 Publication 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 Abbreviated Journal
Volume Issue Pages
Keywords Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration
Abstract Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data.
Address Amiens; France; June 2015
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 (down) ORASIS
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ RLL2015 Serial 2626
Permanent link to this record
 
 
Author Fernando Vilariño; Dimosthenis Karatzas
Title The Library Living Lab Type Conference Article
Year 2015 Publication Open Living Lab Days Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Istanbul; Turkey; August 2015
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 (down) OLLD
Notes MV; DAG;SIAI Approved no
Call Number Admin @ si @ViK2015 Serial 2797
Permanent link to this record
 
 
Author G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva
Title LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task Type Conference Article
Year 2016 Publication 12th NTCIR Conference on Evaluation of Information Access Technologies Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
Address Tokyo; Japan; June 2016
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 (down) NTCIR
Notes MILAB; Approved no
Call Number Admin @ si @OCB2016 Serial 2789
Permanent link to this record
 
 
Author Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin
Title Towards Automatic Concept Transfer Type Conference Article
Year 2011 Publication Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering Abbreviated Journal
Volume Issue Pages 167.176
Keywords chromatic modeling, color concepts, color transfer, concept transfer
Abstract This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study.
Address
Corporate Author Thesis
Publisher ACM Press Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-0907-3 Medium
Area Expedition Conference (down) NPAR
Notes CIC Approved no
Call Number Admin @ si @ MSM2011 Serial 1866
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez
Title Multi-task Bilinear Classifiers for Visual Domain Adaptation Type Conference Article
Year 2013 Publication Advances in Neural Information Processing Systems Workshop Abbreviated Journal
Volume Issue Pages
Keywords Domain Adaptation; Pedestrian Detection; ADAS
Abstract We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines.
Address Lake Tahoe; Nevada; USA; December 2013
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 (down) NIPSW
Notes ADAS; 600.054; 600.057; 601.217;ISE Approved no
Call Number ADAS @ adas @ XRH2013 Serial 2340
Permanent link to this record