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	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
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	Author	Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes
	Title	Learning Graph Edit Distance by Graph NeuralNetworks			Type	Miscellaneous
	Year	2020	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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.
	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	DAG; 600.121; 600.140; 601.302			Approved	no
	Call Number	Admin @ si @ RFL2020			Serial	3555
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	Author	Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
	Title	Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12822	Issue		Pages	306-320
	Keywords
	Abstract	This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
	Address	Lausanne; Suissa; September 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ MRG2021b			Serial	3571
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	Author	Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
	Title	Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12822	Issue		Pages	381–395
	Keywords
	Abstract	In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
	Address	Lausanne; Suissa; September 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	ICDAR
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ RMG2021			Serial	3572
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	Author	Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
	Title	DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12823	Issue		Pages	555–568
	Keywords
	Abstract	Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
	Address	Lausanne; Suissa; September 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ BRL2021a			Serial	3573
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	Author	Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
	Title	Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts			Type	Journal Article
	Year	2021	Publication	International Journal on Document Analysis and Recognition	Abbreviated Journal	IJDAR
	Volume	24	Issue		Pages	269–281
	Keywords
	Abstract	Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art.
	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	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ BRL2021b			Serial	3574
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	Author	Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
	Title	Graph-Based Deep Generative Modelling for Document Layout Generation			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12917	Issue		Pages	525-537
	Keywords
	Abstract	One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
	Address	Lausanne; Suissa; September 2021
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121; 600.140; 110.312			Approved	no
	Call Number	Admin @ si @ BRL2021			Serial	3676
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	Author	Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer
	Title	Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches.			Type	Conference Article
	Year	2020	Publication	International Workshop on Artificial Intelligence for Healthcare Applications	Abbreviated Journal
	Volume	12661	Issue		Pages	476-489
	Keywords
	Abstract	Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.
	Address	Virtual; January 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	ICPRW
	Notes	DAG; 600.121; 600.140;			Approved	no
	Call Number	Admin @ si @ BCF2020			Serial	3508
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	Author	Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor
	Title	A Few-shot Learning Approach for Historical Encoded Manuscript Recognition			Type	Conference Article
	Year	2021	Publication	25th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5413-5420
	Keywords
	Abstract	Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
	Address	Virtual; January 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	ICPR
	Notes	DAG; 600.121; 600.140			Approved	no
	Call Number	Admin @ si @ SFK2021			Serial	3449
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	Author	Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
	Title	Real-time Lexicon-free Scene Text Retrieval			Type	Journal Article
	Year	2021	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	110	Issue		Pages	107656
	Keywords
	Abstract	In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos.
	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	DAG; 600.121; 600.129; 601.338			Approved	no
	Call Number	Admin @ si @ MTD2021			Serial	3493
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	Author	Arka Ujjal Dey; Suman Ghosh; Ernest Valveny
	Title	Don't only Feel Read: Using Scene text to understand advertisements			Type	Conference Article
	Year	2018	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks.
	Address	Salt Lake City; Utah; USA; June 2018
	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	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DGV2018			Serial	3551
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	Author	Mohammed Al Rawi; Dimosthenis Karatzas
	Title	On the Labeling Correctness in Computer Vision Datasets			Type	Conference Article
	Year	2018	Publication	Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Image datasets have heavily been used to build computer vision systems. These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble.
	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	ECML-PKDDW
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ RaK2018			Serial	3144
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	Author	Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas
	Title	Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings			Type	Conference Article
	Year	2018	Publication	International Workshop on Reproducible Research in Pattern Recognition	Abbreviated Journal
	Volume	11455	Issue		Pages	71-82
	Keywords
	Abstract	Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ NDC2018			Serial	3178
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	Author	Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi
	Title	WiCV 2018: The Fourth Women In Computer Vision Workshop			Type	Conference Article
	Year	2018	Publication	4th Women in Computer Vision Workshop	Abbreviated Journal
	Volume		Issue		Pages	1941-19412
	Keywords	Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
	Abstract	We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
	Address	Salt Lake City; USA; June 2018
	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	WiCV
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DBR2018			Serial	3222
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	Author	Marçal Rusiñol; Lluis Gomez
	Title	Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos			Type	Journal
	Year	2018	Publication	Revista anual de la Asociación de Archiveros de Castilla y León	Abbreviated Journal
	Volume	21	Issue		Pages	161-174
	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	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ RuG2018			Serial	3239
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