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Author Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke
Title A Fast Matching Algorithm for Graph-Based Handwriting Recognition Type Conference Article
Year 2013 Publication 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition Abbreviated Journal
Volume 7877 Issue Pages 194-203
Keywords
Abstract The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
Address (up) Vienna; Austria; May 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-38220-8 Medium
Area Expedition Conference GBR
Notes DAG; 600.045; 605.203 Approved no
Call Number Admin @ si @ FSF2013 Serial 2294
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Author L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan
Title Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text Type Conference Article
Year 2009 Publication 15th International Conference on Image Analysis and Processing Abbreviated Journal
Volume 5716 Issue Pages 567-574
Keywords
Abstract An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
Address (up) Vietri sul Mare, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-04145-7 Medium
Area Expedition Conference ICIAP
Notes DAG Approved no
Call Number Admin @ si @ TPS2009 Serial 1871
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Author David Masip; Jordi Vitria
Title Real Time Face Detection and Verification for Uncontrolled Environments Type Miscellaneous
Year 2004 Publication Second COST 275 Workshop Biometrics on the Internet: Fundamentals, Advances and Applications, 55–58. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Vigo
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 @ MaV2004a Serial 446
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Author Santiago Segui; Michal Drozdzal; Petia Radeva; Jordi Vitria
Title An Integrated Approach to Contextual Face Detection Type Conference Article
Year 2012 Publication 1st International Conference on Pattern Recognition Applications and Methods Abbreviated Journal
Volume Issue Pages 143-150
Keywords
Abstract Face detection is, in general, based on content-based detectors. Nevertheless, the face is a non-rigid object with well defined relations with respect to the human body parts. In this paper, we propose to take benefit of the context information in order to improve content-based face detections. We propose a novel framework for integrating multiple content- and context-based detectors in a discriminative way. Moreover, we develop an integrated scoring procedure that measures the ’faceness’ of each hypothesis and is used to discriminate the detection results. Our approach detects a higher rate of faces while minimizing the number of false detections, giving an average increase of more than 10% in average precision when comparing it to state-of-the art face detectors
Address (up) Vilamoura, Algarve, Portugal
Corporate Author Thesis
Publisher Springer 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 ICPRAM
Notes MILAB; OR;MV Approved no
Call Number Admin @ si @ SDR2012 Serial 1895
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Author Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva
Title Uncertainty Modeling and Deep Learning Applied to Food Image Analysis Type Conference Article
Year 2020 Publication 13th International Joint Conference on Biomedical Engineering Systems and Technologies Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis.
Address (up) Villetta; Malta; February 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 BIODEVICES
Notes MILAB Approved no
Call Number Admin @ si @ ANK2020 Serial 3526
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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 (up) 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
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Author Xialei Liu; Chenshen Wu; Mikel Menta; Luis Herranz; Bogdan Raducanu; Andrew Bagdanov; Shangling Jui; Joost Van de Weijer
Title Generative Feature Replay for Class-Incremental Learning Type Conference Article
Year 2020 Publication CLVISION – Workshop on Continual Learning in Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem.
We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning
Address (up) Virtual CVPR
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; 601.309; 602.200; 600.141; 600.120 Approved no
Call Number Admin @ si @ LWM2020 Serial 3419
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Author Yaxing Wang; Abel Gonzalez-Garcia; David Berga; Luis Herranz; Fahad Shahbaz Khan; Joost Van de Weijer
Title MineGAN: effective knowledge transfer from GANs to target domains with few images Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs.
Address (up) Virtual CVPR
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 LAMP; 600.109; 600.141; 600.120 Approved no
Call Number Admin @ si @ WGB2020 Serial 3421
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Author Lu Yu; Bartlomiej Twardowski; Xialei Liu; Luis Herranz; Kai Wang; Yongmai Cheng; Shangling Jui; Joost Van de Weijer
Title Semantic Drift Compensation for Class-Incremental Learning of Embeddings Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Class-incremental learning of deep networks sequentially increases the number of classes to be classified. During training, the network has only access to data of one task at a time, where each task contains several classes. In this setting, networks suffer from catastrophic forgetting which refers to the drastic drop in performance on previous tasks. The vast majority of methods have studied this scenario for classification networks, where for each new task the classification layer of the network must be augmented with additional weights to make room for the newly added classes. Embedding networks have the advantage that new classes can be naturally included into the network without adding new weights. Therefore, we study incremental learning for embedding networks. In addition, we propose a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars. We approximate the drift of previous tasks based on the drift that is experienced by current task data. We perform experiments on fine-grained datasets, CIFAR100 and ImageNet-Subset. We demonstrate that embedding networks suffer significantly less from catastrophic forgetting. We outperform existing methods which do not require exemplars and obtain competitive results compared to methods which store exemplars. Furthermore, we show that our proposed SDC when combined with existing methods to prevent forgetting consistently improves results.
Address (up) Virtual CVPR
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 LAMP; 600.141; 601.309; 602.200; 600.120 Approved no
Call Number Admin @ si @ YTL2020 Serial 3422
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Author Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa
Title Deep Learning Based Corn Kernel Classification Type Conference Article
Year 2020 Publication 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been
performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.
Address (up) Virtual CVPR
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 MSIAU; 600.130; 600.122 Approved no
Call Number Admin @ si @ VMS2020 Serial 3430
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Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi
Title Thermal Image Super-Resolution Challenge – PBVS 2020 Type Conference Article
Year 2020 Publication 16h IEEE Workshop on Perception Beyond the Visible Spectrum Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
Address (up) Virtual CVPR
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 MSIAU; ISE; 600.119; 600.122 Approved no
Call Number Admin @ si @ RSV2020 Serial 3431
Permanent link to this record
 
 
Author Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez
Title Computing the Testing Error Without a Testing Set Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
Address (up) Virtual CVPR
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 HuPBA; no proj Approved no
Call Number Admin @ si @ CEM2020 Serial 3437
Permanent link to this record
 
 
Author Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz
Title Gate-Shift Networks for Video Action Recognition Type Conference Article
Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity.
Address (up) Virtual CVPR
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 HuPBA; no proj Approved no
Call Number Admin @ si @ SEL2020 Serial 3438
Permanent link to this record
 
 
Author Arnau Baro; Alicia Fornes; Carles Badal
Title Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism Type Conference Article
Year 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks.
Address (up) 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; 600.140; 600.121 Approved no
Call Number Admin @ si @ BFB2020 Serial 3448
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 (up) 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