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Author | Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas | ||||
Title | Self‐supervised learning of visual features through embedding images into text topic spaces | Type | Conference Article | ||
Year | 2017 | Publication ![]() |
30th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches. | ||||
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 | DAG; 600.084; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GPR2017 | Serial | 2889 | ||
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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 ![]() |
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. |
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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 | ||
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Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta | ||||
Title | Bag of Negatives for Siamese Architectures | Type | Conference Article | ||
Year | 2019 | Publication ![]() |
30th British Machine Vision Conference | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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 | ||
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Author | Yaxing Wang; L. Zhang; Joost Van de Weijer | ||||
Title | Ensembles of generative adversarial networks | Type | Conference Article | ||
Year | 2016 | Publication ![]() |
30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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. |
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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 | ||
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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 ![]() |
30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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. |
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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 | ||
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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 ![]() |
30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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 | ||
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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 ![]() |
30th ACM International Conference on Multimedia | Abbreviated Journal | |
Volume | Issue | Pages | 6539-6548 | ||
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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 | ||
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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 ![]() |
2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles | Abbreviated Journal | |
Volume | Issue | Pages | 59–64 | ||
Keywords | Intelligent Headlights | ||||
Abstract | |||||
Address | |||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | IROS | ||
Notes | ADAS;CIC | Approved | no | ||
Call Number | ADAS @ adas @ LHB2008b | Serial | 1112 | ||
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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 ![]() |
2nd Workshop on Lifelogging Tools and Applications | Abbreviated Journal | |
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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 | ||
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Author | Javier Vazquez; Robert Benavente; Maria Vanrell | ||||
Title | Naming constraints constancy | Type | Conference Article | ||
Year | 2012 | Publication ![]() |
2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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]. |
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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 | ||
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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 ![]() |
2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | |
Volume | Issue | Pages | |||
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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. |
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | AV A | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ OPD2012a | Serial | 2132 | ||
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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 ![]() |
2nd International Workshop on Solutions for Automatic Gaze Data Analysis | Abbreviated Journal | |
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Address | Bielefeld; Germany; September 2015 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | SAGA | ||
Notes | MV;SIAI | Approved | no | ||
Call Number | Admin @ si @ FLV2015b | Serial | 2676 | ||
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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 ![]() |
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 | ||||
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Publisher | SciTePress | Place of Publication | Editor | Djemal, Khalifa | |
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Area | 800 | Expedition | Conference | MIAD | |
Notes | MV;SIAI | Approved | no | ||
Call Number | IAM @ iam @ BSV2011a | Serial | 1695 | ||
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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 ![]() |
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. |
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Address | Sydney; Australia; September 2019 | ||||
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Area | Expedition | Conference | ICDAR WML | ||
Notes | DAG; 600.140; 601.311; 600.140 | Approved | no | ||
Call Number | Admin @ si @ CMV2019 | Serial | 3353 | ||
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Author | Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf | ||||
Title | Ridgeness for Detecting Lane Markings | Type | Miscellaneous | ||
Year | 2005 | Publication ![]() |
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) | ||||
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Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ LSS2005 | Serial | 548 | ||
Permanent link to this record |