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Статті в журналах з теми "Multimodal retrieval"
Cui, Chenhao, and Zhoujun Li. "Prompt-Enhanced Generation for Multimodal Open Question Answering." Electronics 13, no. 8 (April 10, 2024): 1434. http://dx.doi.org/10.3390/electronics13081434.
Повний текст джерелаXu, Hong. "Multimodal bird information retrieval system." Applied and Computational Engineering 53, no. 1 (March 28, 2024): 96–102. http://dx.doi.org/10.54254/2755-2721/53/20241282.
Повний текст джерелаRomberg, Stefan, Rainer Lienhart, and Eva Hörster. "Multimodal Image Retrieval." International Journal of Multimedia Information Retrieval 1, no. 1 (March 7, 2012): 31–44. http://dx.doi.org/10.1007/s13735-012-0006-4.
Повний текст джерелаKitanovski, Ivan, Gjorgji Strezoski, Ivica Dimitrovski, Gjorgji Madjarov, and Suzana Loskovska. "Multimodal medical image retrieval system." Multimedia Tools and Applications 76, no. 2 (January 25, 2016): 2955–78. http://dx.doi.org/10.1007/s11042-016-3261-1.
Повний текст джерелаKulvinder Singh, Et al. "Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (October 30, 2023): 560–69. http://dx.doi.org/10.17762/ijritcc.v11i9.8844.
Повний текст джерелаCao, Yu, Shawn Steffey, Jianbiao He, Degui Xiao, Cui Tao, Ping Chen, and Henning Müller. "Medical Image Retrieval: A Multimodal Approach." Cancer Informatics 13s3 (January 2014): CIN.S14053. http://dx.doi.org/10.4137/cin.s14053.
Повний текст джерелаRafailidis, D., S. Manolopoulou, and P. Daras. "A unified framework for multimodal retrieval." Pattern Recognition 46, no. 12 (December 2013): 3358–70. http://dx.doi.org/10.1016/j.patcog.2013.05.023.
Повний текст джерелаDong, Bin, Songlei Jian, and Kai Lu. "Learning Multimodal Representations by Symmetrically Transferring Local Structures." Symmetry 12, no. 9 (September 13, 2020): 1504. http://dx.doi.org/10.3390/sym12091504.
Повний текст джерелаZhang, Guihao, and Jiangzhong Cao. "Feature Fusion Based on Transformer for Cross-modal Retrieval." Journal of Physics: Conference Series 2558, no. 1 (August 1, 2023): 012012. http://dx.doi.org/10.1088/1742-6596/2558/1/012012.
Повний текст джерелаKompus, Kristiina, Tom Eichele, Kenneth Hugdahl, and Lars Nyberg. "Multimodal Imaging of Incidental Retrieval: The Low Route to Memory." Journal of Cognitive Neuroscience 23, no. 4 (April 2011): 947–60. http://dx.doi.org/10.1162/jocn.2010.21494.
Повний текст джерелаДисертації з теми "Multimodal retrieval"
Adebayo, Kolawole John <1986>. "Multimodal Legal Information Retrieval." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8634/1/ADEBAYO-JOHN-tesi.pdf.
Повний текст джерелаChen, Jianan. "Deep Learning Based Multimodal Retrieval." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0019.
Повний текст джерелаMultimodal tasks play a crucial role in the progression towards achieving general artificial intelligence (AI). The primary goal of multimodal retrieval is to employ machine learning algorithms to extract relevant semantic information, bridging the gap between different modalities such as visual images, linguistic text, and other data sources. It is worth noting that the information entropy associated with heterogeneous data for the same high-level semantics varies significantly, posing a significant challenge for multimodal models. Deep learning-based multimodal network models provide an effective solution to tackle the difficulties arising from substantial differences in information entropy. These models exhibit impressive accuracy and stability in large-scale cross-modal information matching tasks, such as image-text retrieval. Furthermore, they demonstrate strong transfer learning capabilities, enabling a well-trained model from one multimodal task to be fine-tuned and applied to a new multimodal task, even in scenarios involving few-shot or zero-shot learning. In our research, we develop a novel generative multimodal multi-view database specifically designed for the multimodal referential segmentation task. Additionally, we establish a state-of-the-art (SOTA) benchmark and multi-view metric for referring expression segmentation models in the multimodal domain. The results of our comparative experiments are presented visually, providing clear and comprehensive insights
Böckmann, Christine, Jens Biele, Roland Neuber, and Jenny Niebsch. "Retrieval of multimodal aerosol size distribution by inversion of multiwavelength data." Universität Potsdam, 1997. http://opus.kobv.de/ubp/volltexte/2007/1436/.
Повний текст джерелаZhu, Meng. "Cross-modal semantic-associative labelling, indexing and retrieval of multimodal data." Thesis, University of Reading, 2010. http://centaur.reading.ac.uk/24828/.
Повний текст джерелаKahn, Itamar. "Remembering the past : multimodal imaging of cortical contributions to episodic retrieval." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33171.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references.
What is the nature of the neural processes that allow humans to remember past events? The theoretical framework adopted in this thesis builds upon cognitive models that suggest that episodic retrieval can be decomposed into two classes of computations: (1) recovery processes that serve to reactivate stored memories, making information from a past episode readily available, and (2) control processes that serve to guide the retrieval attempt and monitor/evaluate information arising from the recovery processes. A multimodal imaging approach that combined fMRI and MEG was adopted to gain insight into the spatial and temporal brain mechanisms supporting episodic retrieval. Chapter 1 reviews major findings and theories in the episodic retrieval literature grounding the open questions and controversies within the suggested framework. Chapter 2 describes an fMRI and MEG experiment that identified medial temporal cortical structures that signal item memory strength, thus supporting the perception of item familiarity. Chapter 3 describes an fMRI experiment that demonstrated that retrieval of contextual details involves reactivation of neural patterns engaged at encoding.
(cont.) Further, leveraging this pattern of reactivation, it was demonstrated that false recognition may be accompanied by recollection. The fMRI experiment reported in Chapter 3, when combined with an MEG experiment reported in Chapter 4, directly addressed questions regarding the control processes engaged during episodic retrieval. In particular, Chapter 3 showed that parietal and prefrontal cortices contribute to controlling the act of arriving at a retrieval decision. Chapter 4 then illuminates the temporal characteristics of parietal activation during episodic retrieval, providing novel evidence about the nature of parietal responses and thus constraints on theories of parietal involvement in episodic retrieval. The conducted research targeted distinct aspects of the multi-faceted act of remembering the past. The obtained data contribute to the building of an anatomical and temporal "blueprint" documenting the cascade of neural events that unfold during attempts to remember, as well as when such attempts are met with success or lead to memory errors. In the course of framing this research within the context of cognitive models of retrieval, the obtained neural data reflect back on and constrain these theories of remembering.
by Itamar Kahn.
Ph.D.
Nag, Chowdhury Sreyasi [Verfasser]. "Text-image synergy for multimodal retrieval and annotation / Sreyasi Nag Chowdhury." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1240674139/34.
Повний текст джерелаLolich, María, and Susana Azzollini. "Phenomenological retrieval style of autobiographical memories in a sample of major depressed individuals." Pontificia Universidad Católica del Perú, 2016. http://repositorio.pucp.edu.pe/index/handle/123456789/99894.
Повний текст джерелаLa evocación de recuerdos autobiográficos se caracteriza por presentar distintos compo nentes fenomenológicos. Dada la ausencia de trabajos previos realizados en poblaciones hispanoparlantes, se realizaron 34 entrevistas en profundidad a individuos con y sin tras torno depresivo mayor de la ciudad de Buenos Aires (Argentina). Fueron explorados los componentes fenomenológicos presentes en la evocación de recuerdos autobiográficos significativos. Los datos fueron analizados cualitativamente por medio de la Teoría Fun damentada en los Hechos. Durante el análisis descriptivo, se detectaron siete categorías fenomenológicas emergentes del discurso. Del análisis axial y selectivo fueron identificados dos ejes discursivos: retórico-proposicional y especificidad-generalidad. Las implicancias, en la regulación afectiva, derivadas de la asunción de un estilo amodal o multimodal de proce samiento de información autobiográfica merecen mayor atención.
A evocação de memórias autobiográficas é caracterizada por diferentes componentes feno menológicos. Dada a falta de trabalhos prévios sobre o tema em populações de língua espanhola, 34 entrevistas em profundidade foram conduzidas em indivíduos com e sem transtorno depressivo maior na cidade de Buenos Aires (Argentina). Foram explorados os componentes fenomenológicos presentes na evocação de memórias autobiográficas signi ficativas. Os dados foram analisados qualitativamente através da Teoria Fundamentada. Durante a análise descritiva, foram detectadas sete categorias fenomenológicas emer gentes no discurso. Dos analises axial e seletivo foram identificados dois eixos discursivos: retórico-proposicional e especificidade-generalidade. As implicações, na regulação afetiva, decorrentes da assunção de um estilo amodal ou um estilo multimodal no processamento de informações autobiográficas merecem mais atenção.
Valero-Mas, Jose J. "Towards Interactive Multimodal Music Transcription." Doctoral thesis, Universidad de Alicante, 2017. http://hdl.handle.net/10045/71275.
Повний текст джерелаQuack, Till. "Large scale mining and retrieval of visual data in a multimodal context." Konstanz Hartung-Gorre, 2009. http://d-nb.info/993614620/04.
Повний текст джерелаSaragiotis, Panagiotis. "Cross-modal classification and retrieval of multimodal data using combinations of neural networks." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/843338/.
Повний текст джерелаКниги з теми "Multimodal retrieval"
Müller, Henning, Oscar Alfonso Jimenez del Toro, Allan Hanbury, Georg Langs, and Antonio Foncubierta Rodriguez, eds. Multimodal Retrieval in the Medical Domain. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24471-6.
Повний текст джерелаPeters, Carol, Valentin Jijkoun, Thomas Mandl, Henning Müller, Douglas W. Oard, Anselmo Peñas, Vivien Petras, and Diana Santos, eds. Advances in Multilingual and Multimodal Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85760-0.
Повний текст джерелаJay, Kuo C. C., ed. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, Mass: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаLi, Ying. Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation. Boston, MA: Springer US, 2003.
Знайти повний текст джерелаC, Peters, ed. Advances in multilingual and multimodal information retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007 : revised selected papers. Berlin: Springer, 2008.
Знайти повний текст джерелаForner, Pamela. Multilingual and Multimodal Information Access Evaluation: Second International Conference of the Cross-Language Evaluation Forum, CLEF 2011, Amsterdam, The Netherlands, September 19-22, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.
Знайти повний текст джерелаLi, Ying. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, MA: Kluwer Academic Publishers, 2003.
Знайти повний текст джерелаGosse, Bouma, and SpringerLink (Online service), eds. Interactive Multi-modal Question-Answering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Знайти повний текст джерелаEsposito, Anna. Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues: Third COST 2102 International Training School, Caserta, Italy, March 15-19, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Знайти повний текст джерелаAndrzej, Drygajlo, Esposito Anna, Ortega-Garcia Javier, Faúndez Zanuy Marcos, and SpringerLink (Online service), eds. Biometric ID Management and Multimodal Communication: Joint COST 2101 and 2102 International Conference, BioID_MultiComm 2009, Madrid, Spain, September 16-18, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Знайти повний текст джерелаЧастини книг з теми "Multimodal retrieval"
Mihajlović, Vojkan, Milan Petković, Willem Jonker, and Henk Blanken. "Multimodal Content-based Video Retrieval." In Multimedia Retrieval, 271–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_10.
Повний текст джерелаKitanovski, Ivan, Katarina Trojacanec, Ivica Dimitrovski, and Suzana Loskovska. "Multimodal Medical Image Retrieval." In ICT Innovations 2012, 81–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37169-1_8.
Повний текст джерелаPegia, Maria, Björn Þór Jónsson, Anastasia Moumtzidou, Sotiris Diplaris, Ilias Gialampoukidis, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Multimodal 3D Object Retrieval." In MultiMedia Modeling, 188–201. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53302-0_14.
Повний текст джерелаSchedl, Markus, and Peter Knees. "Personalization in Multimodal Music Retrieval." In Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation, 58–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37425-8_5.
Повний текст джерелаToselli, Alejandro Héctor, Enrique Vidal, and Francisco Casacuberta. "Interactive Image Retrieval." In Multimodal Interactive Pattern Recognition and Applications, 209–26. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-479-1_11.
Повний текст джерелаChang, Edward Y. "Multimodal Fusion." In Foundations of Large-Scale Multimedia Information Management and Retrieval, 121–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20429-6_6.
Повний текст джерелаZhou, Liting, and Cathal Gurrin. "Multimodal Embedding for Lifelog Retrieval." In MultiMedia Modeling, 416–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98358-1_33.
Повний текст джерелаHendriksen, Mariya. "Multimodal Retrieval in E-Commerce." In Lecture Notes in Computer Science, 505–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99739-7_62.
Повний текст джерелаBaeza-Yates, Ricardo. "Retrieval Evaluation in Practice." In Multilingual and Multimodal Information Access Evaluation, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15998-5_2.
Повний текст джерелаNatsev, Apostol (Paul). "Multimodal Search for Effective Video Retrieval." In Lecture Notes in Computer Science, 525–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11788034_60.
Повний текст джерелаТези доповідей конференцій з теми "Multimodal retrieval"
Kalpathy-Cramer, Jayashree, and William Hersh. "Multimodal medical image retrieval." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1743384.1743415.
Повний текст джерелаSlaney, Malcolm. "Multimodal retrieval and ranking." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1743384.1743426.
Повний текст джерелаLisowska, Agnes. "Multimodal interface design for multimodal meeting content retrieval." In the 6th international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027933.1028006.
Повний текст джерелаGasser, Ralph, Luca Rossetto, and Heiko Schuldt. "Multimodal Multimedia Retrieval with vitrivr." In ICMR '19: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3323873.3326921.
Повний текст джерелаAgrawal, Rajeev, William Grosky, and Farshad Fotouhi. "Image Retrieval Using Multimodal Keywords." In 2006 8th IEEE International Symposium on Multimedia. IEEE, 2006. http://dx.doi.org/10.1109/ism.2006.91.
Повний текст джерелаAlsan, Huseyin Fuat, Ekrem Yildiz, Ege Burak Safdil, Furkan Arslan, and Taner Arsan. "Multimodal Retrieval with Contrastive Pretraining." In 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2021. http://dx.doi.org/10.1109/inista52262.2021.9548414.
Повний текст джерелаWehrmann, Jonatas, Mauricio A. Lopes, Martin D. More, and Rodrigo C. Barros. "Fast Self-Attentive Multimodal Retrieval." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00207.
Повний текст джерелаKim, Taeyong, and Bowon Lee. "Multi-Attention Multimodal Sentiment Analysis." In ICMR '20: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372278.3390698.
Повний текст джерелаSingh, Vivek K., Siripen Pongpaichet, and Ramesh Jain. "Situation Recognition from Multimodal Data." In ICMR'16: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2911996.2930061.
Повний текст джерелаLin, Yen-Yu, and Chiou-Shann Fuh. "Multimodal kernel learning for image retrieval." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551790.
Повний текст джерела