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Author Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote
Title MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation Type Conference Article
Year 2022 Publication (up) 29th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords multi-view; cross-view; semantic segmentation; synthetic dataset
Abstract We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 .
Address Bordeaux; France; October2022
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 ICIP
Notes LAMP Approved no
Call Number Admin @ si @ AWW2022 Serial 3781
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Author Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva
Title Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods Type Conference Article
Year 2022 Publication (up) 29th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring
Abstract Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans.
Address Bordeaux; France; October 2022
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 ICIP
Notes MILAB Approved no
Call Number Admin @ si @ SBC2022 Serial 3789
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Author Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji
Title Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding Type Conference Article
Year 2022 Publication (up) 29th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
Keywords Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics
Abstract In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM.
Address Bordeaux; France; October 2022
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 ICIP
Notes MACO Approved no
Call Number Admin @ si @ ZWM2022 Serial 3790
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Author Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil
Title A Novel Cochlear Reference Frame Based On The Laplace Equation Type Conference Article
Year 2015 Publication (up) 29th international Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal
Volume 10 Issue 1 Pages 1-312
Keywords
Abstract Poster
Address Barcelona; Spain; 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 CARS
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ VGG2015 Serial 2615
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias
Title Scene Representations for Autonomous Driving: an approach based on polygonal primitives Type Conference Article
Year 2015 Publication (up) 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 503-515
Keywords Scene reconstruction; Point cloud; Autonomous vehicles
Abstract In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Address Lisboa; Portugal; November 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 ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number Admin @ si @ OSS2015a Serial 2662
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Author J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa
Title Visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication (up) 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Address Lisboa; Portugal; November 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2194-5357 ISBN 978-3-319-27145-3 Medium
Area Expedition Conference ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number Admin @ si @ PAD2015 Serial 2663
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Author Kai Wang; Luis Herranz; Joost Van de Weijer
Title Continual learning in cross-modal retrieval Type Conference Article
Year 2021 Publication (up) 2nd CLVISION workshop Abbreviated Journal
Volume Issue Pages 3628-3638
Keywords
Abstract Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline.
Address Virtual; June 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 CVPRW
Notes LAMP; 600.120; 600.141; 600.147; 601.379 Approved no
Call Number Admin @ si @ WHW2021 Serial 3566
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Author Lasse Martensson; Anders Hast; Alicia Fornes
Title Word Spotting as a Tool for Scribal Attribution Type Conference Article
Year 2017 Publication (up) 2nd Conference of the association of Digital Humanities in the Nordic Countries Abbreviated Journal
Volume Issue Pages 87-89
Keywords
Abstract
Address Gothenburg; Suecia; March 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 978-91-88348-83-8 Medium
Area Expedition Conference DHN
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ MHF2017 Serial 2954
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Author Albin Soutif; Antonio Carta; Joost Van de Weijer
Title Improving Online Continual Learning Performance and Stability with Temporal Ensembles Type Conference Article
Year 2023 Publication (up) 2nd Conference on Lifelong Learning Agents Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature.
Address Montreal; Canada; August 2023
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 COLLAS
Notes LAMP Approved no
Call Number Admin @ si @ SCW2023 Serial 3922
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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
Title LIUM-CVC Submissions for WMT17 Multimodal Translation Task Type Conference Article
Year 2017 Publication (up) 2nd Conference on Machine Translation Abbreviated Journal
Volume Issue Pages
Keywords
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.
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 WMT
Notes LAMP; 600.106; 600.120 Approved no
Call Number Admin @ si @ CAB2017 Serial 3035
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Author Gemma Sanchez; Josep Llados; Enric Marti
Title A string-based method to recognize symbols and structural textures in architectural plans Type Conference Article
Year 1997 Publication (up) 2nd IAPR Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
Address Nancy, 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
Notes DAG; IAM Approved no
Call Number IAM @ iam @ SLE1997 Serial 1498
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Author Fadi Dornaika; Angel Sappa
Title Appearance-based 3D Face Tracker: An Evaluation Study Type Miscellaneous
Year 2005 Publication (up) 2nd IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 121–128 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Beijing (China)
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 ADAS Approved no
Call Number ADAS @ adas @ DoS2005b Serial 580
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Author Jürgen Brauer; Wenjuan Gong; Jordi Gonzalez; Michael Arens
Title On the Effect of Temporal Information on Monocular 3D Human Pose Estimation Type Conference Article
Year 2011 Publication (up) 2nd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Abbreviated Journal
Volume Issue Pages 906 - 913
Keywords
Abstract We address the task of estimating 3D human poses from monocular camera sequences. Many works make use of multiple consecutive frames for the estimation of a 3D pose in a frame. Although such an approach should ease the pose estimation task substantially since multiple consecutive frames allow to solve for 2D projection ambiguities in principle, it has not yet been investigated systematically how much we can improve the 3D pose estimates when using multiple consecutive frames opposed to single frame information. In this paper we analyze the difference in quality of 3D pose estimates based on different numbers of consecutive frames from which 2D pose estimates are available. We validate the use of temporal information on two major different approaches for human pose estimation – modeling and learning approaches. The results of our experiments show that both learning and modeling approaches benefit from using multiple frames opposed to single frame input but that the benefit is small when the 2D pose estimates show a high quality in terms of precision.
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 978-1-4673-0062-9 Medium
Area Expedition Conference ARTEMIS
Notes ISE Approved no
Call Number Admin @ si @BGG 2011 Serial 1860
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Author Gemma Roig; Xavier Boix; Fernando De la Torre
Title Optimal Feature Selection for Subspace Image Matching Type Conference Article
Year 2009 Publication (up) 2nd IEEE International Workshop on Subspace Methods in conjunction Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features.
Address Kyoto, Japan
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 ICCV
Notes Approved no
Call Number Admin @ si @ RBT2009 Serial 1233
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Author Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol
Title Multimodal laughter recognition in video conversations Type Conference Article
Year 2009 Publication (up) 2nd IEEE Workshop on CVPR for Human communicative Behavior analysis Abbreviated Journal
Volume Issue Pages 110–115
Keywords
Abstract Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier.
Address Miami (USA)
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 2160-7508 ISBN 978-1-4244-3994-2 Medium
Area Expedition Conference CVPR
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009c Serial 1188
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