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	Author	Robert Benavente; C. Alejandro Parraga; Maria Vanrell
	Title	La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas			Type	Conference Article
	Year	2010	Publication	9th Congreso Nacional del Color	Abbreviated Journal
	Volume		Issue		Pages	92–95
	Keywords	Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos
	Abstract	En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto.
	Address	Alicante (Spain)
	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	978-84-9717-144-1	Medium
	Area		Expedition		Conference	CNC
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ BPV2010			Serial	1327
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	Author	Riccardo Del Chiaro; Bartlomiej Twardowski; Andrew Bagdanov; Joost Van de Weijer
	Title	Recurrent attention to transient tasks for continual image captioning			Type	Conference Article
	Year	2020	Publication	34th Conference on Neural Information Processing Systems	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones.
	Address	virtual; December 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	NEURIPS
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ CTB2020			Serial	3484
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	Author	Ricardo Toledo; X. Orriols; Petia Radeva; X. Binefa; Jordi Vitria; Cristina Cañero; Juan J. Villanueva
	Title	Eigensnakes for vessel segmentation in angiography.			Type	Conference Article
	Year	2000	Publication	15 th International Conference on Pattern Recognition	Abbreviated Journal
	Volume	4	Issue		Pages	340-343
	Keywords
	Abstract
	Address	Barcelona.
	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	OR;MILAB;ADAS;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ TOR2000			Serial	235
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	Author	Ricardo Toledo; S. Sallent; J. Paradell; Juan J. Villanueva
	Title	CARE: Computer Assisted Radiology Environment			Type	Conference Article
	Year	1995	Publication	Pattern Recognition and image analysis: preprints of the VI National Symposium on Pattern Recogniotion & Image Analysis	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				Approved	no
	Call Number	Admin @ si @			Serial	4025
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	Author	Ricard Coll; Alicia Fornes; Josep Llados
	Title	Graphological Analysis of Handwritten Text Documents for Human Resources Recruitment			Type	Conference Article
	Year	2009	Publication	10th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1081–1085
	Keywords
	Abstract	The use of graphology in recruitment processes has become a popular tool in many human resources companies. This paper presents a model that links features from handwritten images to a number of personality characteristics used to measure applicant aptitudes for the job in a particular hiring scenario. In particular we propose a model of measuring active personality and leadership of the writer. Graphological features that define such a profile are measured in terms of document and script attributes like layout configuration, letter size, shape, slant and skew angle of lines, etc. After the extraction, data is classified using a neural network. An experimental framework with real samples has been constructed to illustrate the performance of the approach.
	Address	Barcelona, Spain
	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	1520-5363	ISBN	978-1-4244-4500-4	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ CFL2009			Serial	1221
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	Author	Ricard Borras; Agata Lapedriza; Laura Igual
	Title	Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition			Type	Conference Article
	Year	2012	Publication	9th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	7325	Issue	II	Pages	98-105
	Keywords
	Abstract	This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems.
	Address	Aveiro, Portugal
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-31297-7	Medium
	Area		Expedition		Conference	ICIAR
	Notes	OR; MILAB;MV			Approved	no
	Call Number	Admin @ si @ BLI2012			Serial	2009
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	Author	Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera
	Title	Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation			Type	Conference Article
	Year	2020	Publication	Bioimage computation workshop	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Virtual; August 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	ECCVW
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ AAF2020			Serial	3520
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	Author	Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera
	Title	Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions			Type	Conference Article
	Year	2019	Publication	Visual Recognition for Medical Images workshop	Abbreviated Journal
	Volume		Issue		Pages	406-415
	Keywords
	Abstract	In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.
	Address	Seul; Korea; October 2019
	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	ICCVW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ AAF2019			Serial	3324
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	Author	Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera
	Title	Deep Frequency Re-Calibration U-Net for Medical Image Segmentation			Type	Conference Article
	Year	2021	Publication	IEEE/CVF International Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages	3274-3283
	Keywords
	Abstract	We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
	Address	VIRTUAL; October 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	ICCVW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ ABA2021			Serial	3645
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	Author	Razieh Rastgoo; Kourosh Kiani; Sergio Escalera; Mohammad Sabokrou
	Title	Sign Language Production: A Review			Type	Conference Article
	Year	2021	Publication	Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	3472-3481
	Keywords
	Abstract	Sign Language is the dominant yet non-primary form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research.
	Address	Virtual; June 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	CVPRW
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ RKE2021b			Serial	3603
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	Author	Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe
	Title	Retrieval Guided Unsupervised Multi-domain Image to Image Translation			Type	Conference Article
	Year	2020	Publication	28th ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data.
	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	ACM
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ GLN2020			Serial	3497
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	Author	Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas
	Title	Learning to Learn from Web Data through Deep Semantic Embeddings			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume	11134	Issue		Pages	514-529
	Keywords
	Abstract	In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.
	Address	Munich; Alemanya; September 2018
	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	DAG; 600.129; 601.338; 600.121			Approved	no
	Call Number	Admin @ si @ GGG2018a			Serial	3175
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	Author	Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas
	Title	Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume	11134	Issue		Pages	530-544
	Keywords
	Abstract	Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis.
	Address	Munich; Alemanya; September 2018
	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	DAG; 600.129; 601.338; 600.121			Approved	no
	Call Number	Admin @ si @ GGG2018b			Serial	3176
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	Author	Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas
	Title	Exploring Hate Speech Detection in Multimodal Publications			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.
	Address	Aspen; March 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	WACV
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ GGG2020a			Serial	3280
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	Author	Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas
	Title	Location Sensitive Image Retrieval and Tagging			Type	Conference Article
	Year	2020	Publication	16th European Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.
	Address	Virtual; August 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	ECCV
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ GGG2020b			Serial	3420
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