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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
url  openurl
  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 (up) 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 edit  url
doi  openurl
  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 (up) Pages  
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  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 edit  openurl
  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 edit  openurl
  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 edit  openurl
  Title Unveiling the Social Impact of AI Type Conference Article
  Year 2020 Publication Workshop at Digital Living Lab Days Conference Abbreviated Journal  
  Volume Issue (up) Pages  
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  Address September 2020  
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  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 Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song edit   pdf
url  doi
openurl 
  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 (up) Pages 2179-2188  
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  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 edit  openurl
  Title 3D Scanning of Capitals at Library Living Lab Type Book Whole
  Year 2019 Publication “Living Lab Projects 2019”. ENoLL. Abbreviated Journal  
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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 Kai Wang; Luis Herranz; Anjan Dutta; Joost Van de Weijer edit   pdf
openurl 
  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 (up) 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;DAG;CIC Approved no  
  Call Number Admin @ si @ WHD2020 Serial 3466  
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez edit   pdf
openurl 
  Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Conference Article
  Year 2020 Publication Women in Geometry and Topology Abbreviated Journal  
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  Address Barcelona; September 2019  
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  Notes IAM; DAG; 600.139; 600.145; 600.121 Approved no  
  Call Number Admin @ si @ GRP2020 Serial 3473  
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Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue (up) Pages  
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  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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