|   | 
Details
   web
Records
Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez
Title Procedural Generation of Videos to Train Deep Action Recognition Networks Type Conference Article
Year 2017 Publication (down) 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 2594-2604
Keywords
Abstract Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
Address Honolulu; Hawaii; July 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 CVPR
Notes ADAS; 600.076; 600.085; 600.118 Approved no
Call Number Admin @ si @ SGC2017 Serial 3051
Permanent link to this record
 

 
Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta
Title Bag of Negatives for Siamese Architectures Type Conference Article
Year 2019 Publication (down) 30th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
Address Cardiff; United Kingdom; September 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 BMVC
Notes CIC; 600.140; 600.118 Approved no
Call Number Admin @ si @ GAB2019b Serial 3263
Permanent link to this record
 

 
Author Yaxing Wang; L. Zhang; Joost Van de Weijer
Title Ensembles of generative adversarial networks Type Conference Article
Year 2016 Publication (down) 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.
Address Barcelona; Spain; December 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 NIPSW
Notes LAMP; 600.068 Approved no
Call Number Admin @ si @ WZW2016 Serial 2905
Permanent link to this record
 

 
Author Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez
Title Invertible conditional gans for image editing Type Conference Article
Year 2016 Publication (down) 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
Address Barcelona; Spain; December 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 NIPSW
Notes LAMP; ADAS; 600.068 Approved no
Call Number Admin @ si @ PWR2016 Serial 2906
Permanent link to this record
 

 
Author Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas
Title Coompetitions in machine learning: case studies Type Conference Article
Year 2016 Publication (down) 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; Spain; December 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 NIPSW
Notes HuPBA Approved no
Call Number Admin @ si @ BEG2016 Serial 2911
Permanent link to this record
 

 
Author Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang
Title SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision Type Conference Article
Year 2022 Publication (down) 30th ACM International Conference on Multimedia Abbreviated Journal
Volume Issue Pages 6539-6548
Keywords
Abstract Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework.
Address Lisboa, Portugal, October 2022
Corporate Author Thesis
Publisher Association for Computing Machinery Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-9203-7 Medium
Area Expedition Conference MM
Notes MACO; 600.161; 601.400 Approved no
Call Number Admin @ si @ XYW2022 Serial 3758
Permanent link to this record
 

 
Author Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat
Title Temporal Coherence Analysis for Intelligent Headlight Control Type Miscellaneous
Year 2008 Publication (down) 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles Abbreviated Journal
Volume Issue Pages 59–64
Keywords Intelligent Headlights
Abstract
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 IROS
Notes ADAS;CIC Approved no
Call Number ADAS @ adas @ LHB2008b Serial 1112
Permanent link to this record
 

 
Author Aniol Lidon; Marc Bolaños; Mariella Dimiccoli; Petia Radeva; Maite Garolera; Xavier Giro
Title Semantic Summarization of Egocentric Photo-Stream Events Type Conference Article
Year 2017 Publication (down) 2nd Workshop on Lifelogging Tools and Applications Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address San Francisco; USA; October 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 978-1-4503-5503-2 Medium
Area Expedition Conference ACMW (LTA)
Notes MILAB; no proj Approved no
Call Number Admin @ si @ LBD2017 Serial 3024
Permanent link to this record
 

 
Author Javier Vazquez; Robert Benavente; Maria Vanrell
Title Naming constraints constancy Type Conference Article
Year 2012 Publication (down) 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Different studies have shown that languages from industrialized cultures
share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by
the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018].
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 AV A
Notes CIC Approved no
Call Number Admin @ si @ VBV2012 Serial 2131
Permanent link to this record
 

 
Author Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco
Title An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction Type Conference Article
Year 2012 Publication (down) 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here.
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 AV A
Notes CIC Approved no
Call Number Admin @ si @ OPD2012a Serial 2132
Permanent link to this record
 

 
Author Onur Ferhat; Arcadi Llanza; Fernando Vilariño
Title Gaze interaction for multi-display systems using natural light eye-tracker Type Conference Article
Year 2015 Publication (down) 2nd International Workshop on Solutions for Automatic Gaze Data Analysis Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Bielefeld; Germany; September 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 SAGA
Notes MV;SIAI Approved no
Call Number Admin @ si @ FLV2015b Serial 2676
Permanent link to this record
 

 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach Type Conference Article
Year 2011 Publication (down) 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems Abbreviated Journal
Volume Issue Pages 62-71
Keywords Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
Abstract In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
Address Rome, Italy
Corporate Author Thesis
Publisher SciTePress Place of Publication Editor Djemal, Khalifa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference MIAD
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011a Serial 1695
Permanent link to this record
 

 
Author Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados
Title End-to-End Handwritten Text Detection and Transcription in Full Pages Type Conference Article
Year 2019 Publication (down) 2nd International Workshop on Machine Learning Abbreviated Journal
Volume 5 Issue Pages 29-34
Keywords Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning
Abstract When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
Address Sydney; Australia; September 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 ICDAR WML
Notes DAG; 600.140; 601.311; 600.140 Approved no
Call Number Admin @ si @ CMV2019 Serial 3353
Permanent link to this record
 

 
Author Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf
Title Ridgeness for Detecting Lane Markings Type Miscellaneous
Year 2005 Publication (down) 2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) Abbreviated Journal
Volume Issue Pages
Keywords lane markings
Abstract
Address Hamburg (Germany)
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 @ LSS2005 Serial 548
Permanent link to this record
 

 
Author David Fernandez; Simone Marinai; Josep Llados; Alicia Fornes
Title Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks Type Conference Article
Year 2013 Publication (down) 2nd International Workshop on Historical Document Imaging and Processing Abbreviated Journal
Volume Issue Pages 36-43
Keywords
Abstract Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information.
Address washington; USA; August 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 978-1-4503-2115-0 Medium
Area Expedition Conference HIP
Notes DAG; 600.056; 600.045; 600.061; 602.006 Approved no
Call Number Admin @ si @ FML2013 Serial 2308
Permanent link to this record