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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	Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva
	Title	Uncertainty Modeling and Deep Learning Applied to Food Image Analysis			Type	Conference Article
	Year	2020	Publication	13th International Joint Conference on Biomedical Engineering Systems and Technologies	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis.
	Address	Villetta; Malta; February 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	BIODEVICES
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ANK2020			Serial	3526
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	Author	Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor
	Title	A Few-shot Learning Approach for Historical Encoded Manuscript Recognition			Type	Conference Article
	Year	2021	Publication	25th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5413-5420
	Keywords
	Abstract	Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
	Address	Virtual; January 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	ICPR
	Notes	DAG; 600.121; 600.140			Approved	no
	Call Number	Admin @ si @ SFK2021			Serial	3449
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	Author	Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes
	Title	A conditional GAN based approach for distorted camera captured documents recovery			Type	Conference Article
	Year	2020	Publication	4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	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	MedPRAI
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ SKF2020			Serial	3450
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	Author	Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
	Title	Recurrent Comparator with attention models to detect counterfeit documents			Type	Conference Article
	Year	2019	Publication	15th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation.
	Address	Sidney; 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; 600.140; 600.121; 601.269			Approved	no
	Call Number	Admin @ si @ BRL2019			Serial	3456
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	Author	Fernando Vilariño
	Title	Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique			Type	Conference Article
	Year	2019	Publication	Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Montreal; Canada; December 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
	Notes	MV; DAG; 600.140; 600.121;SIAI			Approved	no
	Call Number	Admin @ si @ Vil2019a			Serial	3457
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	Author	Fernando Vilariño
	Title	Public Libraries Exploring how technology transforms the cultural experience of people			Type	Conference Article
	Year	2019	Publication	Workshop on Social Impact of AI. Open Living Lab Days Conference.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Thessaloniki; Grecia; 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
	Notes	MV; DAG; 600.140; 600.121;SIAI			Approved	no
	Call Number	Admin @ si @ Vil2019b			Serial	3458
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	Author	Fernando Vilariño
	Title	Unveiling the Social Impact of AI			Type	Conference Article
	Year	2020	Publication	Workshop at Digital Living Lab Days Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	September 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
	Notes	MV; DAG; 600.121; 600.140;SIAI			Approved	no
	Call Number	Admin @ si @ Vil2020			Serial	3459
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	Author	Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras
	Title	Light Direction and Color Estimation from Single Image with Deep Regression			Type	Conference Article
	Year	2020	Publication	London Imaging Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes.
	Address	Virtual; September 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	LIM
	Notes	CIC; 600.118; 600.140;			Approved	no
	Call Number	Admin @ si @ SBV2020			Serial	3460
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	Author	Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras
	Title	Intrinsic Decomposition of Document Images In-the-Wild			Type	Conference Article
	Year	2020	Publication	31st British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW.
	Address	Virtual; September 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	BMVC
	Notes	CIC; 600.087; 600.140; 600.118			Approved	no
	Call Number	Admin @ si @ DSM2020			Serial	3461
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	Author	Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song
	Title	Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval			Type	Conference Article
	Year	2019	Publication	IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2179-2188
	Keywords
	Abstract	In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research.
	Address	Long beach; CA; USA; 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	DAG; 600.140; 600.121; 600.097			Approved	no
	Call Number	Admin @ si @ DRD2019			Serial	3462
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	Author	Fernando Vilariño
	Title	3D Scanning of Capitals at Library Living Lab			Type	Book Whole
	Year	2019	Publication	“Living Lab Projects 2019”. ENoLL.	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	MV; DAG; 600.140; 600.121;SIAI			Approved	no
	Call Number	Admin @ si @ Vil2019c			Serial	3463
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	Author	Xinhang Song; Haitao Zeng; Sixian Zhang; Luis Herranz; Shuqiang Jiang
	Title	Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition			Type	Conference Article
	Year	2020	Publication	28th ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method.
	Address	Virtual; October 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	ACM
	Notes	LAMP; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ SZZ2020			Serial	3465
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	Author	Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer
	Title	Bookworm continual learning: beyond zero-shot learning and continual learning			Type	Conference Article
	Year	2020	Publication	Workshop TASK-CV 2020	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past and future classes are generated. We observe that conditioning the feature generator on attributes can actually harm the continual learning ability, and propose two variants (joint class-attribute conditioning and asymmetric generation) to alleviate this problem.
	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	LAMP; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ WHD2020			Serial	3466
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	Author	Carola Figueroa Flores
	Title	Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency			Type	Book Whole
	Year	2021	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	computer vision; visual saliency; fine-grained object recognition; convolutional neural networks; images classification
	Abstract	For humans, the recognition of objects is an almost instantaneous, precise and extremely adaptable process. Furthermore, we have the innate capability to learn new object classes from only few examples. The human brain lowers the complexity of the incoming data by filtering out part of the information and only processing those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple glance the most important or salient regions from an image. This mechanism can be observed by analyzing on which parts of images subjects place attention; where they fix their eyes when an image is shown to them. The most accurate way to record this behavior is to track eye movements while displaying images. Computational saliency estimation aims to identify to what extent regions or objects stand out with respect to their surroundings to human observers. Saliency maps can be used in a wide range of applications including object detection, image and video compression, and visual tracking. The majority of research in the field has focused on automatically estimating saliency maps given an input image. Instead, in this thesis, we set out to incorporate saliency maps in an object recognition pipeline: we want to investigate whether saliency maps can improve object recognition results. In this thesis, we identify several problems related to visual saliency estimation. First, to what extent the estimation of saliency can be exploited to improve the training of an object recognition model when scarce training data is available. To solve this problem, we design an image classification network that incorporates saliency information as input. This network processes the saliency map through a dedicated network branch and uses the resulting characteristics to modulate the standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive experiments on standard benchmark datasets for fine-grained object recognition, we show that our proposed architecture can significantly improve performance, especially on dataset with scarce training data. Next, we address the main drawback of the above pipeline: SMIC requires an explicit saliency algorithm that must be trained on a saliency dataset. To solve this, we implement a hallucination mechanism that allows us to incorporate the saliency estimation branch in an end-to-end trained neural network architecture that only needs the RGB image as an input. A side-effect of this architecture is the estimation of saliency maps. In experiments, we show that this architecture can obtain similar results on object recognition as SMIC but without the requirement of ground truth saliency maps to train the system. Finally, we evaluated the accuracy of the saliency maps that occur as a sideeffect of object recognition. For this purpose, we use a set of benchmark datasets for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human eye-tracking experiments. Our results show that these saliency maps can obtain competitive results on benchmark saliency maps. On one synthetic saliency dataset this method even obtains the state-of-the-art without the need of ever having seen an actual saliency image for training.
	Address	March 2021
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Joost Van de Weijer;Bogdan Raducanu
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-122714-4-7	Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ Fig2021			Serial	3600
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