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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	Factorization with Missing and Noisy Data			Type	Conference Article
	Year	2006	Publication	6th International Conference on Computational Science	Abbreviated Journal	ICCS´06
	Volume	LNCS 3991	Issue		Pages	555–562
	Keywords
	Abstract
	Address	Reading (United Kingdom)
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2006b			Serial	653
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	An Iterative Multiresolution Scheme for SFM			Type	Conference Article
	Year	2006	Publication	International Conference on Image Analysis and Recognition	Abbreviated Journal	ICIAR 2006
	Volume	LNCS 4141	Issue	1	Pages	804–815
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2006c			Serial	704
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	Motion Segmentation from Feature Trajectories with Missing Data			Type	Conference Article
	Year	2007	Publication	3rd. Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal	IbPRIA 2007
	Volume	LNCS 4477	Issue		Pages	483–490
	Keywords
	Abstract
	Address	Girona (Spain)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	J. Marti et al. (Eds.)
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2007a			Serial	814
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	An Adapted Alternation Approach for Recommender Systems			Type	Conference Article
	Year	2008	Publication	IEEE International Conference on e–Business Engineering,	Abbreviated Journal
	Volume		Issue		Pages	128–135
	Keywords
	Abstract	This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
	Address	Xi’an (Xina)
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ JSL2008e			Serial	1044
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	Author	Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer
	Title	Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains			Type	Conference Article
	Year	2021	Publication	16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications	Abbreviated Journal
	Volume	4	Issue		Pages	163-171
	Keywords
	Abstract	arXiv:2007.12562 Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM).
	Address	Virtual; February 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	VISAPP
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ FRB2021c			Serial	3540
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	Author	Carolina Malagelada; F.De Lorio; Fernando Azpiroz; Santiago Segui; Petia Radeva; Anna Accarino; J.Santos; Juan R. Malagelada
	Title	Intestinal Dysmotility in Patients with Functional Intestinal Disorders Demonstrated by Computer Vision Analysis of Capsule Endoscopy Images			Type	Conference Article
	Year	2010	Publication	18th United European Gastroenterology Week	Abbreviated Journal
	Volume	56	Issue	3	Pages	A19-20
	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	UEGW
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ MLA2010			Serial	1779
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	Author	Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez
	Title	Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	697-716
	Keywords
	Abstract	Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos.
	Address	Amsterdam; The Netherlands; October 2016
	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	ECCV
	Notes	ADAS; 600.076; 600.085			Approved	no
	Call Number	Admin @ si @ SGV2016			Serial	2824
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	Author	Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez
	Title	Procedural Generation of Videos to Train Deep Action Recognition Networks			Type	Conference Article
	Year	2017	Publication	30th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2594-2604
	Keywords
	Abstract	Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
	Address	Honolulu; Hawaii; July 2017
	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	ADAS; 600.076; 600.085; 600.118			Approved	no
	Call Number	Admin @ si @ SGC2017			Serial	3051
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	Author	Cesar Isaza; Joaquin Salas; Bogdan Raducanu
	Title	Toward the Detection of Urban Infrastructures Edge Shadows			Type	Conference Article
	Year	2010	Publication	12th International Conference on Advanced Concepts for Intelligent Vision Systems	Abbreviated Journal
	Volume	6474	Issue	I	Pages	30–37
	Keywords
	Abstract	In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising.
	Address	Sydney, Australia
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	eds. Blanc–Talon et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-17687-6	Medium
	Area		Expedition		Conference	ACIVS
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ ISR2010			Serial	1458
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	Author	Cesar Isaza; Joaquin Salas; Bogdan Raducanu
	Title	Synthetic ground truth dataset to detect shadow cast by static objects in outdoor			Type	Conference Article
	Year	2012	Publication	1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications	Abbreviated Journal
	Volume		Issue		Pages	art. 11
	Keywords
	Abstract	In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software.
	Address	Capri, Italy
	Corporate Author				Thesis
	Publisher	ACM	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-1405-3	Medium
	Area		Expedition		Conference	VIGTA
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ISR2012a			Serial	2037
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	Author	Chee-Kheng Chng; Yuliang Liu; Yipeng Sun; Chun Chet Ng; Canjie Luo; Zihan Ni; ChuanMing Fang; Shuaitao Zhang; Junyu Han; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin
	Title	ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT			Type	Conference Article
	Year	2019	Publication	15th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1571-1576
	Keywords
	Abstract	This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text – RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 – 82.65%, ii) T2.1 – 74.3%, iii) T2.2 – 85.32%, iv) T3.1 – 53.86%, and v) T3.2 – 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.
	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; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ CLS2019			Serial	3340
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	Author	Chen Zhang; Maria del Mar Vila Muñoz; Petia Radeva; Roberto Elosua; Maria Grau; Angels Betriu; Elvira Fernandez-Giraldez; Laura Igual
	Title	Carotid Artery Segmentation in Ultrasound Images			Type	Conference Article
	Year	2015	Publication	Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT2015), Joint MICCAI Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Munich; Germany; October 2015
	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	CVII-STENT
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ZVR2015			Serial	2675
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	Author	Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji
	Title	Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding			Type	Conference Article
	Year	2022	Publication	29th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics
	Abstract	In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM.
	Address	Bordeaux; France; October 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	ICIP
	Notes	MACO			Approved	no
	Call Number	Admin @ si @ ZWM2022			Serial	3790
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	Author	Chenshen Wu; Joost Van de Weijer
	Title	Density Map Distillation for Incremental Object Counting			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	2505-2514
	Keywords
	Abstract	We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods.
	Address	Vancouver; Canada; June 2023
	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	LAMP			Approved	no
	Call Number	Admin @ si @ WuW2023			Serial	3916
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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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