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Author Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez edit   pdf
doi  openurl
  Title Adaptation of a computer programming course to the EXHE requirements: evaluation five years later Type (down) Miscellaneous
  Year 2012 Publication European Journal of Engineering Education Abbreviated Journal  
  Volume 37 Issue 3 Pages 243-254  
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  Notes DAG; CIC; OR; invisible;MV Approved no  
  Call Number Admin @ si @ VBL2012 Serial 2070  
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil edit   pdf
openurl 
  Title Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type (down) Miscellaneous
  Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal  
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  Address Tarragona; juliol 2014  
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  Area Expedition Conference CIDUI  
  Notes IAM; 600.075;DAG Approved no  
  Call Number Admin @ si @ SRM2014 Serial 2458  
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Author Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification Type (down) Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.  
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  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DDT2018 Serial 3085  
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Author Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar edit  openurl
  Title TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces Type (down) Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN.		  
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  Notes DAG; 600.084; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ PGG2018 Serial 3177  
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Author Francisco Cruz; Oriol Ramos Terrades edit  openurl
  Title A probabilistic framework for handwritten text line segmentation Type (down) Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning  
  Abstract We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.		  
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  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ CrR2018 Serial 3253  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title A flexible outlier detector based on a topology given by graph communities Type (down) Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
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  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Learning Graph Edit Distance by Graph NeuralNetworks Type (down) Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
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  Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.  
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  Notes DAG; 600.121; 600.140; 601.302 Approved no  
  Call Number Admin @ si @ RFL2020 Serial 3555  
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Author Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados edit   pdf
openurl 
  Title Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild Type (down) Miscellaneous
  Year 2021 Publication Arxiv Abbreviated Journal  
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  Abstract This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ RDB2021 Serial 3674  
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Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny edit   pdf
url  openurl
  Title Hierarchical multimodal transformers for Multipage DocVQA Type (down) Miscellaneous
  Year 2023 Publication arxiv Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number Admin @ si @ TKV2023 Serial 3836  
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Author Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou edit   pdf
url  openurl
  Title CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition Type (down) Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available.  
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  Notes DAG Approved no  
  Call Number Admin @ si @ DSW2023 Serial 3851  
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