Literatura científica selecionada sobre o tema "Multimodal embedding and retrieval"
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Artigos de revistas sobre o assunto "Multimodal embedding and retrieval"
Kim, Donghyun, Kuniaki Saito, Kate Saenko, Stan Sclaroff e Bryan Plummer. "MULE: Multimodal Universal Language Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11254–61. http://dx.doi.org/10.1609/aaai.v34i07.6785.
Texto completo da fonteKim, Jongseok, Youngjae Yu, Hoeseong Kim e Gunhee Kim. "Dual Compositional Learning in Interactive Image Retrieval". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de maio de 2021): 1771–79. http://dx.doi.org/10.1609/aaai.v35i2.16271.
Texto completo da fonteWang, Di, Xinbo Gao, Xiumei Wang, Lihuo He e Bo Yuan. "Multimodal Discriminative Binary Embedding for Large-Scale Cross-Modal Retrieval". IEEE Transactions on Image Processing 25, n.º 10 (outubro de 2016): 4540–54. http://dx.doi.org/10.1109/tip.2016.2592800.
Texto completo da fonteMerkx, Danny, e Stefan L. Frank. "Learning semantic sentence representations from visually grounded language without lexical knowledge". Natural Language Engineering 25, n.º 4 (julho de 2019): 451–66. http://dx.doi.org/10.1017/s1351324919000196.
Texto completo da fonteOta, Kosuke, Keiichiro Shirai, Hidetoshi Miyao e Minoru Maruyama. "Multimodal Analogy-Based Image Retrieval by Improving Semantic Embeddings". Journal of Advanced Computational Intelligence and Intelligent Informatics 26, n.º 6 (20 de novembro de 2022): 995–1003. http://dx.doi.org/10.20965/jaciii.2022.p0995.
Texto completo da fonteQi, Jidong. "Neurophysiological and psychophysical references for trends in supervised VQA multimodal deep learning: An interdisciplinary meta-analysis". Applied and Computational Engineering 30, n.º 1 (22 de janeiro de 2024): 189–201. http://dx.doi.org/10.54254/2755-2721/30/20230096.
Texto completo da fonteLin, Kaiyi, Xing Xu, Lianli Gao, Zheng Wang e Heng Tao Shen. "Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 07 (3 de abril de 2020): 11515–22. http://dx.doi.org/10.1609/aaai.v34i07.6817.
Texto completo da fonteMithun, Niluthpol C., Juncheng Li, Florian Metze e Amit K. Roy-Chowdhury. "Joint embeddings with multimodal cues for video-text retrieval". International Journal of Multimedia Information Retrieval 8, n.º 1 (12 de janeiro de 2019): 3–18. http://dx.doi.org/10.1007/s13735-018-00166-3.
Texto completo da fonteYang, Bang, Yong Dai, Xuxin Cheng, Yaowei Li, Asif Raza e Yuexian Zou. "Embracing Language Inclusivity and Diversity in CLIP through Continual Language Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 6 (24 de março de 2024): 6458–66. http://dx.doi.org/10.1609/aaai.v38i6.28466.
Texto completo da fonteXu, Tong, Peilun Zhou, Linkang Hu, Xiangnan He, Yao Hu e Enhong Chen. "Socializing the Videos: A Multimodal Approach for Social Relation Recognition". ACM Transactions on Multimedia Computing, Communications, and Applications 17, n.º 1 (16 de abril de 2021): 1–23. http://dx.doi.org/10.1145/3416493.
Texto completo da fonteTeses / dissertações sobre o assunto "Multimodal embedding and retrieval"
Rubio, Romano Antonio. "Fashion discovery : a computer vision approach". Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/672423.
Texto completo da fonteLa interpretación semántica de imágenes del mundo de la moda es sin duda uno de los dominios más desafiantes para la visión por computador. Leves variaciones en color y forma pueden conferir significados o interpretaciones distintas a una imagen. Es un dominio estrechamente ligado a la comprensión humana subjetiva, pero también a la interpretación y reconocimiento de escenarios y contextos. Ser capaz de extraer información específica sobre moda de imágenes e interpretarla de manera correcta puede ser útil en muchas situaciones y puede ayudar a entender la información subyacente en una imagen. Además, la moda es uno de los negocios más importantes a nivel global, con un valor estimado de tres trillones de dólares y un mercado online en constante crecimiento, lo cual aumenta el interés de los algoritmos basados en imágenes para buscar, clasificar o recomendar prendas. Esta tesis doctoral pretende resolver problemas específicos relacionados con el tratamiento de datos de tiendas virtuales de moda, yendo desde la información más básica a nivel de píxel hasta un entendimiento más abstracto que permita extraer conclusiones sobre las prendas presentes en una imagen, aprovechando para ello la Multi-modalidad de los datos disponibles para desarrollar algunas de las soluciones. Las contribuciones incluyen: - Un nuevo método de extracción de superpíxeles enfocado a mejorar el proceso de anotación de imágenes de moda. - La construcción de un espacio común para representar imágenes y textos referentes a moda. - La aplicación de ese espacio en la tarea de identificar el producto principal dentro de una imagen que muestra un conjunto de prendas. En resumen, la moda es un dominio complejo a muchos niveles en términos de visión por computador y aprendizaje automático, y desarrollar algoritmos específicos capaces de capturar la información esencial a partir de imágenes y textos no es una tarea trivial. Con el fin de resolver algunos de los desafíos que esta plantea, y considerando que este es un doctorado industrial, contribuimos al tema con una variedad de soluciones que pueden mejorar el rendimiento de muchas tareas extremadamente útiles para la industria de la moda online
Automàtica, robòtica i visió
Engilberge, Martin. "Deep Inside Visual-Semantic Embeddings". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS150.
Texto completo da fonteNowadays Artificial Intelligence (AI) is omnipresent in our society. The recentdevelopment of learning methods based on deep neural networks alsocalled "Deep Learning" has led to a significant improvement in visual representation models.and textual.In this thesis, we aim to further advance image representation and understanding.Revolving around Visual Semantic Embedding (VSE) approaches, we explore different directions: We present relevant background covering images and textual representation and existing multimodal approaches. We propose novel architectures further improving retrieval capability of VSE and we extend VSE models to novel applications and leverage embedding models to visually ground semantic concept. Finally, we delve into the learning process andin particular the loss function by learning differentiable approximation of ranking based metric
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.
Texto completo da fonteChen, Jianan. "Deep Learning Based Multimodal Retrieval". Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0019.
Texto completo da fonteMultimodal 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 e Jenny Niebsch. "Retrieval of multimodal aerosol size distribution by inversion of multiwavelength data". Universität Potsdam, 1997. http://opus.kobv.de/ubp/volltexte/2007/1436/.
Texto completo da fonteZhu, Meng. "Cross-modal semantic-associative labelling, indexing and retrieval of multimodal data". Thesis, University of Reading, 2010. http://centaur.reading.ac.uk/24828/.
Texto completo da fonteKahn, 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.
Texto completo da fonteThis 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.
Texto completo da fonteLuqman, Muhammad Muzzamil. "Fuzzy multilevel graph embedding for recognition, indexing and retrieval of graphic document images". Thesis, Tours, 2012. http://www.theses.fr/2012TOUR4005/document.
Texto completo da fonteThis thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval
Lolich, María, e 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.
Texto completo da fonteLa 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.
Livros sobre o assunto "Multimodal embedding and retrieval"
Müller, Henning, Oscar Alfonso Jimenez del Toro, Allan Hanbury, Georg Langs e 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.
Texto completo da fontePeters, Carol, Valentin Jijkoun, Thomas Mandl, Henning Müller, Douglas W. Oard, Anselmo Peñas, Vivien Petras e 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.
Texto completo da fonteJay, Kuo C. C., ed. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, Mass: Kluwer Academic Publishers, 2003.
Encontre o texto completo da fonteLi, Ying. Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation. Boston, MA: Springer US, 2003.
Encontre o texto completo da fonteC, 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.
Encontre o texto completo da fonteForner, 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.
Encontre o texto completo da fonteLi, Ying. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, MA: Kluwer Academic Publishers, 2003.
Encontre o texto completo da fonteEsposito, 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.
Encontre o texto completo da fonteGosse, Bouma, e SpringerLink (Online service), eds. Interactive Multi-modal Question-Answering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Encontre o texto completo da fonteAndrzej, Drygajlo, Esposito Anna, Ortega-Garcia Javier, Faúndez Zanuy Marcos e 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.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Multimodal embedding and retrieval"
Zhou, Liting, e 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.
Texto completo da fonteMihajlović, Vojkan, Milan Petković, Willem Jonker e 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.
Texto completo da fonteKitanovski, Ivan, Katarina Trojacanec, Ivica Dimitrovski e 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.
Texto completo da fontePegia, Maria, Björn Þór Jónsson, Anastasia Moumtzidou, Sotiris Diplaris, Ilias Gialampoukidis, Stefanos Vrochidis e 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.
Texto completo da fonteZhang, Xia, Weizheng Chen e Hongfei Yan. "TLINE: Scalable Transductive Network Embedding". In Information Retrieval Technology, 98–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48051-0_8.
Texto completo da fonteSchedl, Markus, e 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.
Texto completo da fonteAbdulahhad, Karam. "Concept Embedding for Information Retrieval". In Lecture Notes in Computer Science, 563–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76941-7_45.
Texto completo da fonteGerritse, Emma J., Faegheh Hasibi e Arjen P. de Vries. "Graph-Embedding Empowered Entity Retrieval". In Lecture Notes in Computer Science, 97–110. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45439-5_7.
Texto completo da fonteToselli, Alejandro Héctor, Enrique Vidal e 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.
Texto completo da fonteVu, Dang-Thinh, e Jason J. Jung. "Detecting Emerging Rumors by Embedding Propagation Graphs". In Information Retrieval Technology, 173–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42835-8_15.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Multimodal embedding and retrieval"
Couairon, Guillaume, Matthijs Douze, Matthieu Cord e Holger Schwenk. "Embedding Arithmetic of Multimodal Queries for Image Retrieval". In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00542.
Texto completo da fonteHuang, Feiran, Xiaoming Zhang, Chaozhuo Li, Zhoujun Li, Yueying He e Zhonghua Zhao. "Multimodal Network Embedding via Attention based Multi-view Variational Autoencoder". In ICMR '18: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206025.3206035.
Texto completo da fonteMithun, Niluthpol Chowdhury, Juncheng Li, Florian Metze e Amit K. Roy-Chowdhury. "Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval". In ICMR '18: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206025.3206064.
Texto completo da fonteHuang, Fei, Yong Cheng, Cheng Jin, Yuejie Zhang e Tao Zhang. "Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval". In SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3077136.3080681.
Texto completo da fonteBhattacharya, Indrani, Arkabandhu Chowdhury e Vikas C. Raykar. "Multimodal Dialog for Browsing Large Visual Catalogs using Exploration-Exploitation Paradigm in a Joint Embedding Space". In ICMR '19: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3323873.3325036.
Texto completo da fonteNeculai, Andrei, Yanbei Chen e Zeynep Akata. "Probabilistic Compositional Embeddings for Multimodal Image Retrieval". In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00501.
Texto completo da fonteParida, Kranti Kumar, Neeraj Matiyali, Tanaya Guha e Gaurav Sharma. "Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zero-shot Classification and Retrieval of Videos". In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093438.
Texto completo da fonteDadas, Slawomir. "OPI at SemEval-2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation". In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.semeval-1.22.
Texto completo da fonteSung, Raymond C. W., James M. Ritchie, Theodore Lim, Aparajithan Sivanathan e Mike J. Chantler. "The Evaluation of a Virtual-Aided Design Engineering Review (VADER) System for Automated Knowledge Capture and Reuse". In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12030.
Texto completo da fonteSzekely, Eniko, Eric Bruno e Stephane Marchand-Maillet. "High-Dimensional Multimodal Distribution Embedding". In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.194.
Texto completo da fonte