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Albert Berenguel, Oriol Ramos Terrades, Josep Llados and Cristina Cañero. 2019. Recurrent Comparator with attention models to detect counterfeit documents. 15th International Conference on Document Analysis and Recognition.
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.
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Fernando Vilariño. 2019. Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique. Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier.
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Fernando Vilariño. 2019. Public Libraries Exploring how technology transforms the cultural experience of people. Workshop on Social Impact of AI. Open Living Lab Days Conference..
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Fernando Vilariño. 2020. Unveiling the Social Impact of AI. Workshop at Digital Living Lab Days Conference.
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Sounak Dey, Pau Riba, Anjan Dutta, Josep Llados and Yi-Zhe Song. 2019. Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval. IEEE Conference on Computer Vision and Pattern Recognition.2179–2188.
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.
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Fernando Vilariño. 2019. 3D Scanning of Capitals at Library Living Lab.
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Debora Gil, Oriol Ramos Terrades and Raquel Perez. 2020. Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution. Women in Geometry and Topology.
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Oriol Ramos Terrades, Albert Berenguel and Debora Gil. 2020. A flexible outlier detector based on a topology given by graph communities.
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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Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez and Dimosthenis Karatzas. 2021. Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval. IEEE Winter Conference on Applications of Computer Vision.4022–4032.
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Andres Mafla, Rafael S. Rezende, Lluis Gomez, Diana Larlus and Dimosthenis Karatzas. 2021. StacMR: Scene-Text Aware Cross-Modal Retrieval. IEEE Winter Conference on Applications of Computer Vision.2219–2229.
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