Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	2266–2280 of 3413 records found matching your query (RSS \| history):

Search & Display Options

Select All Deselect All

[141–150] << 151 152 153 154 155 156 157 158 159 160 >> [161–170]

List View

Citations

Details

	Records
	Author	Eduard Vazquez
	Title	Distribution Characterization using Topological Features. Application to Colour Image Processing			Type	Report
	Year	2007	Publication	CVC Technical Report # 107	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's 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				Approved	no
	Call Number	Admin @ si @ Vaz2009			Serial	1254
Permanent link to this record



	Author	Sudeep Katakol; Basem Elbarashy; Luis Herranz; Joost Van de Weijer; Antonio Lopez
	Title	Distributed Learning and Inference with Compressed Images			Type	Journal Article
	Year	2021	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	30	Issue		Pages	3069 - 3083
	Keywords
	Abstract	Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed. Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g. autonomous vehicles, cloud computing). In addition, many devices suffer from limited resources to store or transmit data (e.g. storage space, channel capacity). In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints. However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process. Moreover, we may only have compressed images at training time but are able to use original images at inference time, or vice versa, and in such a case, the downstream model suffers from covariate shift. In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario. We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate. In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs). Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resource-limited devices. The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.
	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
	Notes	LAMP; ADAS; 600.120; 600.118			Approved	no
	Call Number	Admin @ si @ KEH2021			Serial	3543
Permanent link to this record



	Author	Pau Riba
	Title	Distilling Structure from Imagery: Graph-based Models for the Interpretation of Document Images			Type	Book Whole
	Year	2020	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in \(\mathbb{R}^n\), is not properly defined for graphs. In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Josep Llados;Alicia Fornes
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-121011-6-4	Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ Rib20			Serial	3478
Permanent link to this record



	Author	Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui
	Title	Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data			Type	Conference Article
	Year	2022	Publication	10th International Conference on Learning Representations	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data. Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID.
	Address	Virtual
	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	ICLR
	Notes	LAMP; 600.147			Approved	no
	Call Number	Admin @ si @ WWY2022			Serial	3791
Permanent link to this record



	Author	Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas
	Title	Distilling Content from Style for Handwritten Word Recognition			Type	Conference Article
	Year	2020	Publication	17th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes. Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both the generative process and the recognition efficiency in the IAM dataset.
	Address	Virtual ICFHR; September 2020
	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	ICFHR
	Notes	DAG; 600.129; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ KRR2020			Serial	3425
Permanent link to this record



	Author	Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi
	Title	Distance Learning for Similarity Estimation			Type	Journal
	Year	2008	Publication	IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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
	Notes	ADAS;MILAB			Approved	no
	Call Number	ADAS @ adas @ JAS2008			Serial	961
Permanent link to this record



	Author	Jordi Vitria
	Title	Disseny de sistemes (intel.ligents) de visio.			Type	Miscellaneous
	Year	1996	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ Vit1996a			Serial	88
Permanent link to this record



	Author	A. Martinez
	Title	Disseny d´agents autonoms.			Type	Miscellaneous
	Year	1994	Publication	Graduating Project	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis	Master's 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				Approved	no
	Call Number	Admin @ si @ Mar1994			Serial	236
Permanent link to this record



	Author	Fernando Vilariño
	Title	Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation			Type	Conference Article
	Year	2016	Publication	International Workshop on Poetry: Archives, Poetries and Receptions	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; Spain; October 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	POETRY
	Notes	MV; 600.097;SIAI			Approved	no
	Call Number	Admin @ si @Vil2016b			Serial	2805
Permanent link to this record



	Author	David Berga; Marc Masana; Joost Van de Weijer
	Title	Disentanglement of Color and Shape Representations for Continual Learning			Type	Conference Article
	Year	2020	Publication	ICML Workshop on Continual Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance.
	Address	Virtual; July 2020
	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	ICMLW
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ BMW2020			Serial	3506
Permanent link to this record



	Author	Diego Porres
	Title	Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks			Type	Conference Article
	Year	2021	Publication	Machine Learning for Creativity and Design, Neurips Workshop	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL.
	Address	Virtual; December 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	NEURIPSW
	Notes	ADAS; 601.365			Approved	no
	Call Number	Admin @ si @ Por2021			Serial	3597
Permanent link to this record



	Author	Carlos Boned Riera; Oriol Ramos Terrades
	Title	Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2186-2191
	Keywords	Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition
	Abstract	Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.
	Address	Montreal; Quebec; Canada; August 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	ICPR
	Notes	DAG; 600.121; 600.162			Approved	no
	Call Number	Admin @ si @ BoR2022			Serial	3741
Permanent link to this record



	Author	Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez
	Title	Discriminative Compact Pyramids for Object and Scene Recognition			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	4	Pages	1627-1636
	Keywords
	Abstract	Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; CAT;CIC			Approved	no
	Call Number	Admin @ si @ EKW2012			Serial	1807
Permanent link to this record



	Author	Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat
	Title	Discriminative Color Descriptors			Type	Conference Article
	Year	2013	Publication	IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2866 - 2873
	Keywords
	Abstract	Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.
	Address	Portland; Oregon; June 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	1063-6919	ISBN		Medium
	Area		Expedition		Conference	CVPR
	Notes	CIC; 600.048			Approved	no
	Call Number	Admin @ si @ KWK2013a			Serial	2262
Permanent link to this record



	Author	Xose M. Pardo; Petia Radeva; D. Cabello
	Title	Discriminant Snakes for 3D Reconstruction of Anatomical Organs			Type	Journal
	Year	2003	Publication	Medical Image Analysis, 7(3): 293–310 (IF: 4.442)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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
	Notes	MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ PPC2003			Serial	398
Permanent link to this record