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	Author	Eduardo Aguilar; Petia Radeva
	Title	Food Recognition by Integrating Local and Flat Classifiers			Type	Conference Article
	Year	2019	Publication	9th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	11867	Issue		Pages	65-74
	Keywords
	Abstract	The recognition of food image is an interesting research topic, in which its applicability in the creation of nutritional diaries stands out with the aim of improving the quality of life of people with a chronic disease (e.g. diabetes, heart disease) or prone to acquire it (e.g. people with overweight or obese). For a food recognition system to be useful in real applications, it is necessary to recognize a huge number of different foods. We argue that for very large scale classification, a traditional flat classifier is not enough to acquire an acceptable result. To address this, we propose a method that performs prediction with local classifiers, based on a class hierarchy, or with flat classifier. We decide which approach to use, depending on the analysis of both the Epistemic Uncertainty obtained for the image in the children classifiers and the prediction of the parent classifier. When our criterion is met, the final prediction is obtained with the respective local classifier; otherwise, with the flat classifier. From the results, we can see that the proposed method improves the classification performance compared to the use of a single flat classifier.
	Address	Madrid; July 2019
	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	IbPRIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2019b			Serial	3369
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	Author	Parichehr Behjati Ardakani; Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Jordi Gonzalez
	Title	Catastrophic interference in Disguised Face Recognition			Type	Conference Article
	Year	2019	Publication	9th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	11868	Issue		Pages	64-75
	Keywords	Neural network forgetness; Face recognition; Disguised Faces
	Abstract	It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain.
	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	IbPRIA
	Notes	ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ AVG2019			Serial	3416
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	Author	Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
	Title	16th International Conference, 2021, Proceedings, Part III			Type	Book Whole
	Year	2021	Publication	Document Analysis and Recognition – ICDAR 2021	Abbreviated Journal
	Volume	12823	Issue		Pages
	Keywords
	Abstract	This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
	Address	Lausanne, Switzerland, September 5-10, 2021
	Corporate Author				Thesis
	Publisher	Springer Cham	Place of Publication		Editor	Josep Llados; Daniel Lopresti; Seiichi Uchida
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-030-86333-3	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @			Serial	3727
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	Author	Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
	Title	16th International Conference, 2021, Proceedings, Part IV			Type	Book Whole
	Year	2021	Publication	Document Analysis and Recognition – ICDAR 2021	Abbreviated Journal
	Volume	12824	Issue		Pages
	Keywords
	Abstract	This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
	Address	Lausanne, Switzerland, September 5-10, 2021
	Corporate Author				Thesis
	Publisher	Springer Cham	Place of Publication		Editor	Josep Llados; Daniel Lopresti; Seiichi Uchida
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-030-86336-4	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @			Serial	3728
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	Author	Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera
	Title	FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition			Type	Conference Article
	Year	2020	Publication	ECCV Workshops	Abbreviated Journal
	Volume	12540	Issue		Pages	463-481
	Keywords
	Abstract	This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too.
	Address	Virtual; August 2020
	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	ECCVW
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ SJB2020			Serial	3499
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	Author	Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva
	Title	Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams			Type	Conference Article
	Year	2020	Publication	ECCV Workshops	Abbreviated Journal
	Volume	12538	Issue		Pages	469-484
	Keywords
	Abstract	The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.
	Address	Virtual; August 2020
	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	ECCVW
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ MTM2020			Serial	3528
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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	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	Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski
	Title	Metric Learning for Session-Based Recommendations			Type	Conference Article
	Year	2021	Publication	43rd edition of the annual BCS-IRSG European Conference on Information Retrieval	Abbreviated Journal
	Volume	12656	Issue		Pages	650-665
	Keywords	Session-based recommendations; Deep metric learning; Learning to rank
	Abstract	Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.
	Address	Virtual; March 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	ECIR
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ TZZ2021			Serial	3586
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	Author	Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
	Title	16th International Conference, 2021, Proceedings, Part I			Type	Book Whole
	Year	2021	Publication	Document Analysis and Recognition – ICDAR 2021	Abbreviated Journal
	Volume	12821	Issue		Pages
	Keywords
	Abstract	This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition.
	Address	Lausanne, Switzerland, September 5-10, 2021
	Corporate Author				Thesis
	Publisher	Springer Cham	Place of Publication		Editor	Josep Llados; Daniel Lopresti; Seiichi Uchida
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-030-86548-1	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @			Serial	3725
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	Author	Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
	Title	16th International Conference, 2021, Proceedings, Part II			Type	Book Whole
	Year	2021	Publication	Document Analysis and Recognition – ICDAR 2021	Abbreviated Journal
	Volume	12822	Issue		Pages
	Keywords
	Abstract	This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports. The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
	Address	Lausanne, Switzerland, September 5-10, 2021
	Corporate Author				Thesis
	Publisher	Springer Cham	Place of Publication		Editor	Josep Llados; Daniel Lopresti; Seiichi Uchida
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-030-86330-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @			Serial	3726
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	Author	Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes
	Title	A Transcription Is All You Need: Learning to Align through Attention			Type	Conference Article
	Year	2021	Publication	14th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume	12916	Issue		Pages	141–146
	Keywords
	Abstract	Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.
	Address	Virtual; 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	GREC
	Notes	DAG; 602.230; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ TSC2021			Serial	3619
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	Author	Ruben Tito; Dimosthenis Karatzas; Ernest Valveny
	Title	Document Collection Visual Question Answering			Type	Conference Article
	Year	2021	Publication	16th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	12822	Issue		Pages	778-792
	Keywords	Document collection; Visual Question Answering
	Abstract	Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.
	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	ICDAR
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ TKV2021			Serial	3622
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	Author	Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus
	Title	Mobile eHealth Platform for Home Monitoring of Bipolar Disorder			Type	Conference Article
	Year	2021	Publication	27th ACM International Conference on Multimedia Modeling	Abbreviated Journal
	Volume	12573	Issue		Pages	330-341
	Keywords
	Abstract	People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur. MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.
	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	MMM
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ CEE2021			Serial	3659
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	Author	Henry Velesaca; Patricia Suarez; Dario Carpio; Angel Sappa
	Title	Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy			Type	Conference Article
	Year	2021	Publication	16th International Symposium on Visual Computing	Abbreviated Journal
	Volume	13017	Issue		Pages	131–143
	Keywords
	Abstract	This paper presents a complete pipeline to perform deep learning-based instance segmentation of different types of grains (e.g., corn, sunflower, soybeans, lentils, chickpeas, mote, and beans). The proposed approach consists of using synthesized image datasets for the training process, which are easily generated according to the category of the instance to be segmented. The synthesized imaging process allows generating a large set of well-annotated grain samples with high variability—as large and high as the user requires. Instance segmentation is performed through a popular deep learning based approach, the Mask R-CNN architecture, but any learning-based instance segmentation approach can be considered. Results obtained by the proposed pipeline show that the strategy of using synthesized image datasets for training instance segmentation helps to avoid the time-consuming image annotation stage, as well as to achieve higher intersection over union and average precision performances. Results obtained with different varieties of grains are shown, as well as comparisons with manually annotated images, showing both the simplicity of the process and the improvements in the performance.
	Address	Virtual; October 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	ISVC
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ VSC2021			Serial	3667
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