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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	Arnau Baro; Pau Riba; Alicia Fornes
	Title	A Starting Point for Handwritten Music Recognition			Type	Conference Article
	Year	2018	Publication	1st International Workshop on Reading Music Systems	Abbreviated Journal
	Volume		Issue		Pages	5-6
	Keywords	Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA
	Abstract	In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community.
	Address	Paris; France; September 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	WORMS
	Notes	DAG; 600.097; 601.302; 601.330; 600.121			Approved	no
	Call Number	Admin @ si @ BRF2018			Serial	3223
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	Author	Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza
	Title	Deep Learning models for passability detection of flooded roads			Type	Conference Article
	Year	2018	Publication	MediaEval 2018 Multimedia Benchmark Workshop	Abbreviated Journal
	Volume	2283	Issue		Pages
	Keywords
	Abstract	In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.
	Address	Sophia Antipolis; France; October 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	MediaEval
	Notes	LAMP; 600.084; 600.109; 600.120			Approved	no
	Call Number	Admin @ si @ LFS2018			Serial	3224
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	Author	Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari
	Title	Objects as context for detecting their semantic parts			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	6907 - 6916
	Keywords	Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection
	Abstract	We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
	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	CVPR
	Notes	LAMP; 600.109; 600.120			Approved	no
	Call Number	Admin @ si @ GMF2018			Serial	3229
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	Author	Gemma Rotger; Felipe Lumbreras; Francesc Moreno-Noguer; Antonio Agudo
	Title	2D-to-3D Facial Expression Transfer			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2008 - 2013
	Keywords
	Abstract	Automatically changing the expression and physical features of a face from an input image is a topic that has been traditionally tackled in a 2D domain. In this paper, we bring this problem to 3D and propose a framework that given an input RGB video of a human face under a neutral expression, initially computes his/her 3D shape and then performs a transfer to a new and potentially non-observed expression. For this purpose, we parameterize the rest shape –obtained from standard factorization approaches over the input video– using a triangular mesh which is further clustered into larger macro-segments. The expression transfer problem is then posed as a direct mapping between this shape and a source shape, such as the blend shapes of an off-the-shelf 3D dataset of human facial expressions. The mapping is resolved to be geometrically consistent between 3D models by requiring points in specific regions to map on semantic equivalent regions. We validate the approach on several synthetic and real examples of input faces that largely differ from the source shapes, yielding very realistic expression transfers even in cases with topology changes, such as a synthetic video sequence of a single-eyed cyclops.
	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	ICPR
	Notes	ADAS; 600.086; 600.130; 600.118			Approved	no
	Call Number	Admin @ si @ RLM2018			Serial	3232
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	Author	Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas
	Title	Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets			Type	Conference Article
	Year	2018	Publication	International Workshop on Artificial Intelligence and Pattern Recognition	Abbreviated Journal
	Volume	11047	Issue		Pages	34-42
	Keywords	Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
	Abstract	The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
	Address	Cuba; 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	IWAIPR
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ BGÑ2018			Serial	3237
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	Author	Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer
	Title	LIUM-CVC Submissions for WMT18 Multimodal Translation Task			Type	Conference Article
	Year	2018	Publication	3rd Conference on Machine Translation	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR.
	Address	Brussels; Belgium; October 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	WMT
	Notes	LAMP; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ CBB2018			Serial	3240
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	Author	Lu Yu; Yongmei Cheng; Joost Van de Weijer
	Title	Weakly Supervised Domain-Specific Color Naming Based on Attention			Type	Conference Article
	Year	2018	Publication	24th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3019 - 3024
	Keywords
	Abstract	The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
	Address	Beijing; August 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	ICPR
	Notes	LAMP; 600.109; 602.200; 600.120			Approved	no
	Call Number	Admin @ si @ YCW2018			Serial	3243
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	Author	Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal
	Title	Results of the use of Kahoot! gamification tool in a course of Chemistry			Type	Conference Article
	Year	2018	Publication	4th International Conference on Higher Education Advances	Abbreviated Journal
	Volume		Issue		Pages	1215-1222
	Keywords
	Abstract	The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
	Address	Valencia; 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	HEAD
	Notes	MV; no proj			Approved	no
	Call Number	Admin @ si @ ABN2018			Serial	3246
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	Author	Chenshen Wu; Luis Herranz; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu
	Title	Memory Replay GANs: Learning to Generate New Categories without Forgetting			Type	Conference Article
	Year	2018	Publication	32nd Annual Conference on Neural Information Processing Systems	Abbreviated Journal
	Volume		Issue		Pages	5966-5976
	Keywords
	Abstract	Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion. We first show that sequential fine tuning renders the network unable to properly generate images from previous categories (ie forgetting). Addressing this problem, we propose Memory Replay GANs (MeRGANs), a conditional GAN framework that integrates a memory replay generator. We study two methods to prevent forgetting by leveraging these replays, namely joint training with replay and replay alignment. Qualitative and quantitative experimental results in MNIST, SVHN and LSUN datasets show that our memory replay approach can generate competitive images while significantly mitigating the forgetting of previous categories.
	Address	Montreal; Canada; December 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	NIPS
	Notes	LAMP; 600.106; 600.109; 602.200; 600.120			Approved	no
	Call Number	Admin @ si @ WHL2018			Serial	3249
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	Author	Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska
	Title	Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation			Type	Conference Article
	Year	2018	Publication	International MICCAI Brainlesion Workshop	Abbreviated Journal
	Volume	11384	Issue		Pages	393-405
	Keywords	Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution
	Abstract	In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
	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	MICCAIW
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ PSH2018			Serial	3251
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	Author	Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund
	Title	Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data			Type	Conference Article
	Year	2019	Publication	14th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	123-130
	Keywords	Rain Removal; Traffic Surveillance; Image Denoising
	Abstract	Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance.
	Address	Praga; Czech Republic; February 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	VISIGRAPP
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ BVL2019			Serial	3256
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	Author	Julio C. S. Jacques Junior; Cagri Ozcinar; Marina Marjanovic; Xavier Baro; Gholamreza Anbarjafari; Sergio Escalera
	Title	On the effect of age perception biases for real age regression			Type	Conference Article
	Year	2019	Publication	14th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.
	Address	Lille; France; May 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	FG
	Notes	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ JOM2019			Serial	3262
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	Author	Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta
	Title	Bag of Negatives for Siamese Architectures			Type	Conference Article
	Year	2019	Publication	30th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
	Address	Cardiff; United Kingdom; September 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	BMVC
	Notes	CIC; 600.140; 600.118			Approved	no
	Call Number	Admin @ si @ GAB2019b			Serial	3263
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	Author	Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas
	Title	Selective Style Transfer for Text			Type	Conference Article
	Year	2019	Publication	15th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	805-812
	Keywords	transfer; text style transfer; data augmentation; scene text detection
	Abstract	This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
	Address	Sydney; Australia; September 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	ICDAR
	Notes	DAG; 600.129; 600.135; 601.338; 601.310; 600.121			Approved	no
	Call Number	GBG2019			Serial	3265
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