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Author David Guillamet; B. Moghaddam edit  openurl
  Title 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 (up) Admin @ si @ GuM2002 Serial 293  
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Author R. Herault; Franck Davoine; Fadi Dornaika; Y. Grandvalet edit  openurl
  Title Simultaneous and robust face and facial action tracking Type Miscellaneous
  Year 2006 Publication 15eme Congres Francophone AFRIF–AFIA de Reconnaissance des Formes et Intelligence Artificielle (RFIA´06) Abbreviated Journal  
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  Address Tours (France)  
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  Notes Approved no  
  Call Number (up) Admin @ si @ HDD2006 Serial 735  
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Author Luis Herranz; Weiqing Min; Shuqiang Jiang edit  openurl
  Title Food recognition and recipe analysis: integrating visual content, context and external knowledge Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation and restaurant context as emerging directions.  
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  Notes LAMP; 600.120 Approved no  
  Call Number (up) Admin @ si @ HMJ2018 Serial 3250  
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Author Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane edit   pdf
url  openurl
  Title TopoX: A Suite of Python Packages for Machine Learning on Topological Domains Type Miscellaneous
  Year 2024 Publication Arxiv Abbreviated Journal  
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  Abstract We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL.  
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  Notes HUPBA Approved no  
  Call Number (up) Admin @ si @ HPF2024 Serial 4021  
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Author Laura Igual; Xavier Baro edit   pdf
openurl 
  Title Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación Type Miscellaneous
  Year 2013 Publication Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación, de las XIX Jornadas sobre la Enseñanza Universitaria de la Informática Abbreviated Journal  
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  Area Expedition Conference JENUI  
  Notes OR;HuPBA;MV Approved no  
  Call Number (up) Admin @ si @ IgB2013 Serial 2257  
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Author Zhong Jin; Franck Davoine; Zhen Lou edit  openurl
  Title Facial expression analysis by using KPCA Type Miscellaneous
  Year 2003 Publication IEEE International Conference on Robotics, Intelligent Systems and Signal Processing (IEEE RISSP 2003), pp736–741 Abbreviated Journal  
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  Address Changsha, Hunan, China  
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  Notes Approved no  
  Call Number (up) Admin @ si @ JDL2003 Serial 431  
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Author Zhong Jin; Franck Davoine; Zhen Lou edit  openurl
  Title An Effective EM Algorithm for PCA Mixture Model Type Miscellaneous
  Year 2004 Publication Structural and Statistical Pattern Recognition, Lecture Notes in Computer Science, 3138:626–634 Abbreviated Journal  
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  Address Lisbon, Portugal  
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  Notes Approved no  
  Call Number (up) Admin @ si @ JDL2004 Serial 482  
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Author Zhong Jin; Franck Davoine edit  openurl
  Title Orthogonal ICA Representation Of Images Type Miscellaneous
  Year 2004 Publication 8th International Conference on Control, Automation, Robotics and Vision, 369–374 Abbreviated Journal  
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  Address Kunming (China)  
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  Notes Approved no  
  Call Number (up) Admin @ si @ JiD2004 Serial 499  
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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 edit  url
openurl 
  Title Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract 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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  Notes HUPBA Approved no  
  Call Number (up) Admin @ si @ JKS2018 Serial 3095  
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Author Zhong Jin; Zhen Lou; Jing-Yu Yang; Quan-sen Sun edit  openurl
  Title Face detection using template matching and skin color information Type Miscellaneous
  Year 2005 Publication International Conference on Intelligent Computing, 636–645 Abbreviated Journal  
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  Address Hefei (China)  
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  Notes Approved no  
  Call Number (up) Admin @ si @ JLY2005 Serial 627  
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Author Carme Julia edit  isbn
openurl 
  Title Missig Data Matrix Factorization Addressing the Structure from Motion Problem Type Miscellaneous
  Year 2008 Publication CVC–UAB Abbreviated Journal  
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  Address Bellaterra  
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  ISSN ISBN 978–84–935251–6–3 Medium  
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  Notes Approved no  
  Call Number (up) Admin @ si @ Jul2008 Serial 1104  
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Author Zhong Jin; Jing-Yu Yang; Zhen Lou edit  openurl
  Title A luminance-conditional distribution model of skin color information Type Miscellaneous
  Year 2005 Publication 2005 Beijing International Conference on Imaging: Technology and Applications for the 21th Century, 280–281 Abbreviated Journal  
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  Address Beijing (China)  
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  Notes Approved no  
  Call Number (up) Admin @ si @ JYL2005 Serial 628  
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Author Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig edit  openurl
  Title CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. Type Miscellaneous
  Year 2018 Publication Arxiv Abbreviated Journal  
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  Abstract Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.  
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  Notes MILAB; no proj Approved no  
  Call Number (up) Admin @ si @ KJR2018 Serial 3235  
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Author Mert Kilickaya; Joost van de Weijer; Yuki M. Asano edit   pdf
url  openurl
  Title Towards Label-Efficient Incremental Learning: A Survey Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: this https URL.  
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  Notes LAMP Approved no  
  Call Number (up) Admin @ si @ KWA2023 Serial 3994  
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Author Lubomir Latchev; Maya Dimitrova; David Rotger edit  openurl
  Title A Classifier of Technical Diagnostic States of Electrocardiograph Type Miscellaneous
  Year 2006 Publication International Conference on Computer Systems and Technologies (CompSysTech´06), 15.1–15.6 Abbreviated Journal  
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  Address University of Veliko Tarnovo (Bulgaria)  
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  Notes Approved no  
  Call Number (up) Admin @ si @ LDR2006 Serial 774  
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