| Records |
Author  |
J.M. Sanchez; X. Binefa; J.R. Kender |
| Title |
Multiple Feature Temporal Models for Object Detection in Video. |
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Miscellaneous |
| Year |
2002 |
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Proceeding of the International Conference on Multimedia and Expo ICME 2002 |
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Lausanne |
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no |
| Call Number |
Admin @ si @ SBK2002b |
Serial |
299 |
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| Author |
J.M. Sanchez; X. Binefa |
| Title |
Automatic digital TV commercial recognition. |
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Miscellaneous |
| Year |
1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes, 1: 313–320 |
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Bilbao. |
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no |
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Admin @ si @ SaV1999 |
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181 |
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| Author |
J.M. Sanchez; X. Binefa |
| Title |
Semantics from motion in news videos. |
Type |
Miscellaneous |
| Year |
2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:79–84. |
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no |
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Admin @ si @ SBi2001 |
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210 |
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| Author |
J.L.Bruguera; R.Casado; M.Martinez; I.Corral; Enric Marti; L.A.Branda |
| Title |
El apoyo institucional como elemento favorecedor de la coordinación docente: experiencias en diferentes universidades |
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Miscellaneous |
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2009 |
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Revista Red-U |
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IAM |
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no |
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IAM @ iam @ BCM2009 |
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1490 |
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| Author |
J. Suri; S. Singh; S. Laxminarayan; R. Cesar; H. Jelinek; Petia Radeva; B. Roysam; Ch. Stewart; K. Frizche; J. Williams; H. Tek |
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A Note on Future Research in Vascular and Plaque Segmentation |
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Miscellaneous |
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2003 |
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Angiography and Plaque Imaging: Advanced Segmentation Techniques, Editorial: CRC, pp.501–522, ISBN: 0849317401 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ SSL2003 |
Serial |
419 |
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| Author |
J. Mauri; E. Esplugas; B. Garcia del Blanco; E Fernandez-Nofrerias; A. Cequier; J.A. Gomez-Hospital; Petia Radeva; Ricardo Toledo; Cristina Cañero; Juan J. Villanueva |
| Title |
3-D Stent and Vessel Reconstruction from IVUS: a Physics-Based Approach |
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Miscellaneous |
| Year |
2000 |
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European Heart Journal, Vol.21, XXII Congreso of the European Society of Cardiology |
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MILAB;ADAS |
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no |
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BCNPCL @ bcnpcl @ MEG2000 |
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440 |
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| Author |
J. Mauri; E Fernandez-Nofrerias; Petia Radeva; V. Valle |
| Title |
Ecografia intracoronaria, una ajuda o un mestre en lintervencionisme coronari?" |
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Miscellaneous |
| Year |
2000 |
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Revista de la Societat Catalana de Cardiologia, 3, 435–443. |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ MFR2000 |
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437 |
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| Author |
J. Mauri; E Fernandez-Nofrerias; E. Esplugas; A. Cequier; David Rotger; Ricardo Toledo; Petia Radeva; ; Juan J. Villanueva |
| Title |
Ecografia Intracoronaria: Navegacion Informatica por el cubo de datos de las imagenes. |
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Miscellaneous |
| Year |
2000 |
Publication |
Congreso de la Sociedad Española de Cardiologia. |
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Granada. |
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MILAB;ADAS |
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no |
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BCNPCL @ bcnpcl @ MNE2000 |
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356 |
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| Author |
J. Filipe; Juan Andrade; J.L. Ferrier |
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FAF 2005 |
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Miscellaneous |
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2005 |
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Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, INSTICC Press |
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Barcelona (Spain) |
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no |
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Admin @ si @ FAF2005 |
Serial |
609 |
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| Author |
Ignasi Rius; Javier Varona; Jordi Gonzalez; Juan J. Villanueva |
| Title |
Action Spaces for Efficient Bayesian Tracking of Human Motion |
Type |
Miscellaneous |
| Year |
2006 |
Publication |
International Conference on Pattern Recognition (ICPR´06), 1: 472–475 |
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Hong Kong (China) |
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no |
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ISE @ ise @ RVG2006 |
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770 |
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| Author |
Ignasi Rius; Dani Rowe; Jordi Gonzalez; Xavier Roca |
| Title |
3D Action Modeling and Reconstruction for 2D Human Body Tracking |
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Miscellaneous |
| Year |
2005 |
Publication |
3rd International Conference on Advances in Pattern Recognition (ICAPR’2005), Pattern Recognition and Image Analysis, LNCS 3687: 146–154, ISBN 978–3–540–28833–6 |
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Bath (United Kingdom) |
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ISE |
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no |
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ISE @ ise @ RRG2005c |
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578 |
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| Author |
Hugo Prol; Vincent Dumoulin; Luis Herranz |
| Title |
Cross-Modulation Networks for Few-Shot Learning |
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Miscellaneous |
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2018 |
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Arxiv |
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A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. |
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LAMP; 600.120 |
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no |
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Admin @ si @ PDH2018 |
Serial |
3248 |
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| Author |
Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier |
| Title |
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos |
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Miscellaneous |
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2018 |
Publication |
Arxiv |
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Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. |
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HUPBA |
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no |
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Admin @ si @ JKS2018 |
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3095 |
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| Author |
Hao Wu; Alejandro Ariza-Casabona; Bartłomiej Twardowski; Tri Kurniawan Wijaya |
| Title |
MM-GEF: Multi-modal representation meet collaborative filtering |
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Miscellaneous |
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2023 |
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ARXIV |
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In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods. |
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LAMP |
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Admin @ si @ WAT2023 |
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3988 |
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| Author |
Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
| Title |
Monitoring War Destruction from Space: A Machine Learning Approach |
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Miscellaneous |
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2020 |
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Arxiv |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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ADAS; 600.118 |
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Admin @ si @ MGH2020 |
Serial |
3489 |
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