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Author Maya Dimitrova; I. Terziev; Petia Radeva; Juan J. Villanueva
Title (up) Java-Servlet Technology for Building New Web Document Classifiers Type Miscellaneous
Year 2004 Publication Abbreviated Journal
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Address Varna (Bulgaria)
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DTR2004 Serial 476
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Author David Guillamet; B. Moghaddam
Title (up) Joint Distribution of Local Image Features for Appearance Moldeling. Type Miscellaneous
Year 2002 Publication Proceedings of the IAPR Workshop on Machine Vision Applications MVA 2002. Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ GuM2002 Serial 293
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Author David Lloret; C. Mariño; Joan Serrat; Antonio Lopez; Juan J. Villanueva
Title (up) Landmark-based registration of full SLO video sequences. Type Miscellaneous
Year 2001 Publication Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis,1:189–194. Abbreviated Journal
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Notes ADAS Approved no
Call Number ADAS @ adas @ LMS2001 Serial 116
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Author G.Estape; Enric Marti
Title (up) L’ús d’aplicacions de visualització 3D com a eina d’aprenenetatge en activitats formatives dirigides i autònomes: el cas del programa Bluestar Type Miscellaneous
Year 2008 Publication V Jornades d’Innovació Docent UAB Abbreviated Journal
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Notes IAM Approved no
Call Number IAM @ iam @ ESM2008 Serial 1495
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Author A. Pujol; H. Wechsler; Juan J. Villanueva
Title (up) Learning and Caricaturing the Face Space Using Self-Organization and Hebbian Learning for Face Processing. Type Miscellaneous
Year 2001 Publication 11th International Conference on Image Analysis and Processing, ICIAP 2001, 273–278. Abbreviated Journal
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Address Italia.
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Notes Approved no
Call Number ISE @ ise @ PWV2001 Serial 205
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes
Title (up) Learning Graph Edit Distance by Graph NeuralNetworks Type Miscellaneous
Year 2020 Publication Arxiv Abbreviated Journal
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Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.
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Notes DAG; 600.121; 600.140; 601.302 Approved no
Call Number Admin @ si @ RFL2020 Serial 3555
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Author Mikel Menta; Adriana Romero; Joost Van de Weijer
Title (up) Learning to adapt class-specific features across domains for semantic segmentation Type Miscellaneous
Year 2020 Publication Arxiv Abbreviated Journal
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Abstract arXiv:2001.08311
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image translation networks, which often contain a huge amount of domain-specific parameters. Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide sharp results. In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. To that aim, we design a conditional pixel-wise discriminator network, whose output is conditioned on the segmentation masks. Moreover, following recent advances in image translation, we adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network. Preliminary results on a segmentation task designed to assess the effectiveness of the proposed approach highlight the potential of the model, improving upon strong baselines and alternative designs.
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Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ MRW2020 Serial 3545
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Author Oriol Ramos Terrades; Ernest Valveny
Title (up) Line Detection Using Ridgelets Transform for Graphic Symbol Representation Type Miscellaneous
Year 2003 Publication In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:829–837 Abbreviated Journal
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Address Springer-Verlag
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Notes DAG Approved no
Call Number DAG @ dag @ RaV2003a Serial 403
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Author X. Binefa; J.M. Sanchez; Petia Radeva; Jordi Vitria
Title (up) Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains. Type Miscellaneous
Year 2000 Publication Journal of Visual Languages and Computing, 11(3):253–271. Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ BRS2000 Serial 337
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Author J.M. Sanchez; X. Binefa; Jordi Vitria; Petia Radeva
Title (up) Local Analysis for Scene Break Detection Applied to TV Commercials Recognition. Type Miscellaneous
Year 1999 Publication Visual information and information systems, 237–244, Springer– Verlag. Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ SBV1999 Serial 27
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Author B. Moghaddam; David Guillamet; Jordi Vitria
Title (up) Local Appearance-Based Models using High-Order Statistics of Image Features Type Miscellaneous
Year 2003 Publication Mitsubishi Electrical Reasearch Lab Technical Report Abbreviated Journal
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Call Number BCNPCL @ bcnpcl @ TR2003-85 Serial 396
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Author David Guillamet; Jordi Vitria
Title (up) Local Discriminant Regions Using Support Vector Machines for Object Recognition. Type Miscellaneous
Year 2000 Publication Advances in Pattern Recognition, Lecture Notes in Computer Science 1876: 550–559, Springer Verlag. Abbreviated Journal
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Call Number BCNPCL @ bcnpcl @ GuV2000 a Serial 240
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Author Oriol Ramos Terrades; Ernest Valveny
Title (up) Local Norm Features based on ridgelets Transform Type Miscellaneous
Year 2005 Publication 8th International Conference on Document Analysis and Recognition (ICDAR´05), 700–704 Abbreviated Journal
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Call Number DAG @ dag @ RaV2005d Serial 642
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Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer
Title (up) Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method Type Miscellaneous
Year 2022 Publication Arxiv Abbreviated Journal
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Abstract We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL.
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Notes LAMP; 600.147 Approved no
Call Number Admin @ si @ YWW2022b Serial 3815
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Author Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados
Title (up) Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild Type Miscellaneous
Year 2021 Publication Arxiv Abbreviated Journal
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Abstract This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.
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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ RDB2021 Serial 3674
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