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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	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation			Type	Conference Article
	Year	2013	Publication	12th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	265-269
	Keywords
	Abstract	In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods.
	Address	Washington; USA; August 2013
	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		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DTR2013b			Serial	2331
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Document noise removal using sparse representations over learned dictionary			Type	Conference Article
	Year	2013	Publication	Symposium on Document engineering	Abbreviated Journal
	Volume		Issue		Pages	161-168
	Keywords
	Abstract	best paper award In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
	Address	Barcelona; October 2013
	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	978-1-4503-1789-4	Medium
	Area		Expedition		Conference	ACM-DocEng
	Notes	DAG; 600.061			Approved	no
	Call Number	Admin @ si @ DTR2013a			Serial	2330
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Text/graphic separation using a sparse representation with multi-learned dictionaries			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Graphics Recognition; Layout Analysis; Document Understandin
	Abstract	In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.
	Address	Tsukuba
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DTR2012a			Serial	2135
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Noise suppression over bi-level graphical documents using a sparse representation			Type	Conference Article
	Year	2012	Publication	Colloque International Francophone sur l'Écrit et le Document	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bordeaux
	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	CIFED
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DTR2012b			Serial	2136
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors			Type	Conference Article
	Year	2014	Publication	11th IAPR International Workshop on Document Analysis and Systems	Abbreviated Journal
	Volume		Issue		Pages	156-160
	Keywords
	Abstract	This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.
	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	978-1-4799-3243-6	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ DTR2014			Serial	2543
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	Author	T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades; A.T. Thierry
	Title	Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles			Type	Conference Article
	Year	2008	Publication	Colloque International Francophone sur l'Ecrit et le Document	Abbreviated Journal
	Volume		Issue		Pages	79-84
	Keywords
	Abstract
	Address	Rouen, France
	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	CIFED
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ NTR2008b			Serial	1875
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	Author	T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval			Type	Conference Article
	Year	2008	Publication	Proceedings of the 8th IAPR International Workshop on Document Analysis Systems,	Abbreviated Journal
	Volume		Issue		Pages	191-197
	Keywords
	Abstract
	Address	Nara, Japan
	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	DAS
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ NTR2008a			Serial	1873
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	Author	Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz
	Title	LSTA: Long Short-Term Attention for Egocentric Action Recognition			Type	Conference Article
	Year	2019	Publication	32nd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	9946-9955
	Keywords
	Abstract	Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state-of-the-art performance on four standard benchmarks.
	Address	California; June 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	CVPR
	Notes	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ SEL2019			Serial	3333
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	Author	Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz
	Title	Gate-Shift Networks for Video Action Recognition			Type	Conference Article
	Year	2020	Publication	33rd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity.
	Address	Virtual CVPR
	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	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ SEL2020			Serial	3438
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	Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
	Title	3D Texton Spaces for color-texture retrieval			Type	Conference Article
	Year	2010	Publication	7th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	6111	Issue		Pages	354–363
	Keywords
	Abstract	Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	A.C. Campilho and M.S. Kamel
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-13771-6	Medium
	Area		Expedition		Conference	ICIAR
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ ASV2010a			Serial	1325
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	Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
	Title	Perceptual color texture codebooks for retrieving in highly diverse texture datasets			Type	Conference Article
	Year	2010	Publication	20th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	866–869
	Keywords
	Abstract	Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel.
	Address	Istanbul (Turkey)
	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	1051-4651	ISBN	978-1-4244-7542-1	Medium
	Area		Expedition		Conference	ICPR
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ ASV2010b			Serial	1426
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	Author	Suman Ghosh; Lluis Gomez; Dimosthenis Karatzas; Ernest Valveny
	Title	Efficient indexing for Query By String text retrieval			Type	Conference Article
	Year	2015	Publication	6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015	Abbreviated Journal
	Volume		Issue		Pages	1236 - 1240
	Keywords
	Abstract	This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings.
	Address	Nancy; France; August 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	CBDAR
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ GGK2015			Serial	2693
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	Author	Suman Ghosh; Ernest Valveny
	Title	Query by String word spotting based on character bi-gram indexing			Type	Conference Article
	Year	2015	Publication	13th International Conference on Document Analysis and Recognition ICDAR2015	Abbreviated Journal
	Volume		Issue		Pages	881-885
	Keywords
	Abstract	In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets
	Address	Nancy; France; August 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	ICDAR
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ GhV2015a			Serial	2715
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	Author	Suman Ghosh; Ernest Valveny
	Title	A Sliding Window Framework for Word Spotting Based on Word Attributes			Type	Conference Article
	Year	2015	Publication	Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015	Abbreviated Journal
	Volume	9117	Issue		Pages	652-661
	Keywords	Word spotting; Sliding window; Word attributes
	Abstract	In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.
	Address	Santiago de Compostela; June 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-19389-2	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ GhV2015b			Serial	2716
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