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Author B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols
Title Knowledge graph based methods for record linkage Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 127-133
Keywords
Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
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Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ GRP2020 Serial 3453
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Author Mohamed Ali Souibgui; Y.Kessentini
Title DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement Type Journal Article
Year 2022 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 44 Issue 3 Pages 1180-1191
Keywords
Abstract Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.
Address 1 March 2022
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Notes DAG; 602.230; 600.121; 600.140 Approved no
Call Number Admin @ si @ SoK2022 Serial 3454
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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal
Title Evaluation of the Effect of Improper Segmentation on Word Spotting Type Journal Article
Year 2019 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 22 Issue Pages 361-374
Keywords
Abstract Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.
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Notes DAG; 600.097; 600.084; 600.121; 600.140; 600.129 Approved no
Call Number Admin @ si @ DNL2019 Serial 3455
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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
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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
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Address Montreal; Canada; December 2019
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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
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Address Thessaloniki; Grecia; September 2019
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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
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Abstract
Address September 2020
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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
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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
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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
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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
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Notes MV; DAG; 600.140; 600.121;SIAI Approved no
Call Number Admin @ si @ Vil2019c Serial 3463
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Author Yaxing Wang; Abel Gonzalez-Garcia; Luis Herranz; Joost Van de Weijer
Title Controlling biases and diversity in diverse image-to-image translation Type Journal Article
Year 2021 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 202 Issue Pages 103082
Keywords
Abstract JCR 2019 Q2, IF=3.121
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.
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Notes LAMP; 600.141; 600.109; 600.147 Approved no
Call Number Admin @ si @ WGH2021 Serial 3464
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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
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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
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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
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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
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Area Expedition Conference ECCVW
Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ WHD2020 Serial 3466
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Author Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez
Title Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy Type Journal Article
Year 2020 Publication IEEE Access Abbreviated Journal ACCESS
Volume 8 Issue Pages 159696 - 159704
Keywords
Abstract A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route.
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Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ GEB2020 Serial 3467
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