Relevant bibliographies by topics / Multimodal Information Retrieval

Academic literature on the topic 'Multimodal Information Retrieval'

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Published: 25 May 2024

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Journal articles on the topic "Multimodal Information Retrieval"

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Xu, 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.

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Multimodal bird information retrieval system can help people popularize bird knowledge and help bird conservation. In this paper, we use the self-built bird dataset, the ViT-B/32 model in CLIP model as the training model, python as the development language, and PyQT5 to complete the interface development. The system mainly realizes the uploading and displaying of bird pictures, the multimodal retrieval function of bird information, and the introduction of related bird information. The results of the trial run show that the system can accomplish the multimodal retrieval of bird information, retrieve the species of birds and other related information through the pictures uploaded by the user, or retrieve the most similar bird information through the text content described by the user.
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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.

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Multimodal open question answering involves retrieving relevant information from both images and their corresponding texts given a question and then generating the answer. The quality of the generated answer heavily depends on the quality of the retrieved image–text pairs. Existing methods encode and retrieve images and texts, inputting the retrieved results into a language model to generate answers. These methods overlook the semantic alignment of image–text pairs within the information source, which affects the encoding and retrieval performance. Furthermore, these methods are highly dependent on retrieval performance, and poor retrieval quality can lead to poor generation performance. To address these issues, we propose a prompt-enhanced generation model, PEG, which includes generating supplementary descriptions for images to provide ample material for image–text alignment while also utilizing vision–language joint encoding to improve encoding effects and thereby enhance retrieval performance. Contrastive learning is used to enhance the model’s ability to discriminate between relevant and irrelevant information sources. Moreover, we further explore the knowledge within pre-trained model parameters through prefix-tuning to generate background knowledge relevant to the questions, offering additional input for answer generation and reducing the model’s dependency on retrieval performance. Experiments conducted on the WebQA and MultimodalQA datasets demonstrate that our model outperforms other baseline models in retrieval and generation performance.
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Kulvinder 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.

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Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval.
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UbaidullahBokhari, Mohammad, and Faraz Hasan. "Multimodal Information Retrieval: Challenges and Future Trends." International Journal of Computer Applications 74, no. 14 (July 26, 2013): 9–12. http://dx.doi.org/10.5120/12951-9967.

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Calumby, Rodrigo Tripodi. "Diversity-oriented Multimodal and Interactive Information Retrieval." ACM SIGIR Forum 50, no. 1 (June 27, 2016): 86. http://dx.doi.org/10.1145/2964797.2964811.

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S. Gomathy, K. P. Deepa, T. Revathi, and L. Maria Michael Visuwasam. "Genre Specific Classification for Information Search and Multimodal Semantic Indexing for Data Retrieval." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 01, no. 01 (April 5, 2013): 10–15. http://dx.doi.org/10.9756/sijcsea/v1i1/01010159.

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ZHANG, Jing. "Video retrieval model based on multimodal information fusion." Journal of Computer Applications 28, no. 1 (July 10, 2008): 199–201. http://dx.doi.org/10.3724/sp.j.1087.2008.00199.

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Mourão, André, Flávio Martins, and João Magalhães. "Multimodal medical information retrieval with unsupervised rank fusion." Computerized Medical Imaging and Graphics 39 (January 2015): 35–45. http://dx.doi.org/10.1016/j.compmedimag.2014.05.006.

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Revuelta-Martínez, Alejandro, Luis Rodríguez, Ismael García-Varea, and Francisco Montero. "Multimodal interaction for information retrieval using natural language." Computer Standards & Interfaces 35, no. 5 (September 2013): 428–41. http://dx.doi.org/10.1016/j.csi.2012.11.002.

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Chen, Xu, Alfred O. Hero, III, and Silvio Savarese. "Multimodal Video Indexing and Retrieval Using Directed Information." IEEE Transactions on Multimedia 14, no. 1 (February 2012): 3–16. http://dx.doi.org/10.1109/tmm.2011.2167223.

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Dissertations / Theses on the topic "Multimodal Information Retrieval"

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Adebayo, Kolawole John <1986&gt. "Multimodal Legal Information Retrieval." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8634/1/ADEBAYO-JOHN-tesi.pdf.

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The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely manner. The thesis explored approaches for semantic information retrieval (IR) in the Legal context with a technique that maps specific parts of a text to the relevant concept. This technique relies on text segments, using the Latent Dirichlet Allocation (LDA), a topic modeling algorithm for performing text segmentation, expanding the concept using some Natural Language Processing techniques, and then associating the text segments to the concepts using a semi-supervised text similarity technique. This solves two problems, i.e., that of user specificity in formulating query, and information overload, for querying a large document collection with a set of concepts is more fine-grained since specific information, rather than full documents is retrieved. The second part of the thesis describes our Neural Network Relevance Model for E-Discovery Information Retrieval. Our algorithm is essentially a feature-rich Ensemble system with different component Neural Networks extracting different relevance signal. This model has been trained and evaluated on the TREC Legal track 2010 data. The performance of our models across board proves that it capture the semantics and relatedness between query and document which is important to the Legal Information Retrieval domain.
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Valero-Mas, Jose J. "Towards Interactive Multimodal Music Transcription." Doctoral thesis, Universidad de Alicante, 2017. http://hdl.handle.net/10045/71275.

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La transcripción de música por computador es de vital importancia en tareas del llamo campo de la Extracción y recuperación de información musical por su utilidad como proceso para la obtención de una abstracción simbólica que codifica el contenido musical de un fichero de audio. En esta disertación se estudia este problema desde una perspectiva diferente a la típicamente considerada para estos problemas, la perspectiva interactiva y multimodal. En este paradigma el usuario cobra especial importancia puesto que es parte activa en la resolución del problema (interactividad); por otro lado, la multimodalidad implica que diferentes fuentes de información extraídas de la misma señal se aúnan para ayudar a una mejor resolución de la tarea.
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Fedel, Gabriel de Souza. "Busca multimodal para apoio à pesquisa em biodiversidade." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275751.

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Orientador: Cláudia Maria Bauzer Medeiros
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-18T07:07:49Z (GMT). No. of bitstreams: 1 Fedel_GabrieldeSouza_M.pdf: 14390093 bytes, checksum: 63058da33a22121e927f1cdbaff297d3 (MD5) Previous issue date: 2011
Resumo: A pesquisa em computação aplicada à biodiversidade apresenta muitos desafios, que vão desde o grande volume de dados altamente heterogêneos até a variedade de tipos de usuários. Isto gera a necessidade de ferramentas versáteis de recuperação. As ferramentas disponíveis ainda são limitadas e normalmente só consideram dados textuais, deixando de explorar a potencialidade da busca por dados de outra natureza, como imagens ou sons. Esta dissertação analisa os problemas de realizar consultas multimodais a partir de predicados que envolvem texto e imagem para o domínio de biodiversidade, especificando e implementando um conjunto de ferramentas para processar tais consultas. As contribuições do trabalho, validado com dados reais, incluem a construção de uma ontologia taxonômica associada a nomes vulgares e a possibilidade de apoiar dois perfis de usuários (especialistas e leigos). Estas características estendem o escopo da consultas atualmente disponíveis em sistemas de biodiversidade. Este trabalho está inserido no projeto Bio-CORE, uma parceria entre pesquisadores de computação e biologia para criar ferramentas computacionais para dar apoio à pesquisa em biodiversidade
Abstract: Research on Computing applied to biodiversity present several challenges, ranging from the massive volumes of highly heterogeneous data to the variety in user profiles. This kind of scenario requires versatile data retrieval and management tools. Available tools are still limited. Most often, they only consider textual data and do not take advantage of the multiple data types available, such as images or sounds. This dissertation discusses issues concerning multimodal queries that involve both text and images as search parameters, for the domanin of biodiversity. It presents the specification and implementation of a set of tools to process such queries, which were validate with real data from Unicamp's Zoology Museum. The aim contributions also include the construction of a taxonomic ontology that includes species common names, and support to both researchers and non-experts in queries. Such features extend the scop of queries available in biodiversity information systems. This research is associated with the Biocore project, jointly conducted by researchers in computing and biology, to design and develop computational tools to support research in biodiversity
Mestrado
Banco de Dados
Mestre em Ciência da Computação
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Quack, Till. "Large scale mining and retrieval of visual data in a multimodal context." Konstanz Hartung-Gorre, 2009. http://d-nb.info/993614620/04.

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Calumby, Rodrigo Tripodi 1985. "Recuperação multimodal de imagens com realimentação de relevância baseada em programação genética." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275814.

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Orientador: Ricardo da Silva Torres
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-16T05:18:58Z (GMT). No. of bitstreams: 1 Calumby_RodrigoTripodi_M.pdf: 15749586 bytes, checksum: 2493b0b703adc1973eeabf7eb70ad21c (MD5) Previous issue date: 2010
Resumo: Este trabalho apresenta uma abordagem para recuperação multimodal de imagens com realimentação de relevância baseada em programação genética. Supõe-se que cada imagem da coleção possui informação textual associada (metadado, descrição textual, etc.), além de ter suas propriedades visuais (por exemplo, cor e textura) codificadas em vetores de características. A partir da informação obtida ao longo das iterações de realimentação de relevância, programação genética é utilizada para a criação de funções de combinação de medidas de similaridades eficazes. Com essas novas funções, valores de similaridades diversos são combinados em uma única medida, que mais adequadamente reflete as necessidades do usuário. As principais contribuições deste trabalho consistem na proposta e implementação de dois arcabouços. O primeiro, RFCore, é um arcabouço genérico para atividades de realimentação de relevância para manipulação de objetos digitais. O segundo, MMRFGP, é um arcabouço para recuperação de objetos digitais com realimentação de relevância baseada em programação genética, construído sobre o RFCore. O método proposto de recuperação multimodal de imagens foi validado sobre duas coleções de imagens, uma desenvolvida pela Universidade de Washington e outra da ImageCLEF Photographic Retrieval Task. A abordagem proposta mostrou melhores resultados para recuperação multimodal frente a utilização das modalidades isoladas. Além disso, foram obtidos resultados para recuperação visual e multimodal melhores do que as melhores submissões para a ImageCLEF Photographic Retrieval Task 2008
Abstract: This work presents an approach for multimodal content-based image retrieval with relevance feedback based on genetic programming. We assume that there is textual information (e.g., metadata, textual descriptions) associated with collection images. Furthermore, image content properties (e.g., color and texture) are characterized by image descriptores. Given the information obtained over the relevance feedback iterations, genetic programming is used to create effective combination functions that combine similarities associated with different features. Hence using these new functions the different similarities are combined into a unique measure that more properly meets the user needs. The main contribution of this work is the proposal and implementation of two frameworks. The first one, RFCore, is a generic framework for relevance feedback tasks over digital objects. The second one, MMRF-GP, is a framework for digital object retrieval with relevance feedback based on genetic programming and it was built on top of RFCore. We have validated the proposed multimodal image retrieval approach over 2 datasets, one from the University of Washington and another from the ImageCLEF Photographic Retrieval Task. Our approach has yielded the best results for multimodal image retrieval when compared with one-modality approaches. Furthermore, it has achieved better results for visual and multimodal image retrieval than the best submissions for ImageCLEF Photographic Retrieval Task 2008
Mestrado
Sistemas de Recuperação da Informação
Mestre em Ciência da Computação
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SIMONETTA, FEDERICO. "MUSIC INTERPRETATION ANALYSIS. A MULTIMODAL APPROACH TO SCORE-INFORMED RESYNTHESIS OF PIANO RECORDINGS." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/918909.

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This Thesis discusses the development of technologies for the automatic resynthesis of music recordings using digital synthesizers. First, the main issue is identified in the understanding of how Music Information Processing (MIP) methods can take into consideration the influence of the acoustic context on the music performance. For this, a novel conceptual and mathematical framework named “Music Interpretation Analysis” (MIA) is presented. In the proposed framework, a distinction is made between the “performance” – the physical action of playing – and the “interpretation” – the action that the performer wishes to achieve. Second, the Thesis describes further works aiming at the democratization of music production tools via automatic resynthesis: 1) it elaborates software and file formats for historical music archiving and multimodal machine-learning datasets; 2) it explores and extends MIP technologies; 3) it presents the mathematical foundations of the MIA framework and shows preliminary evaluations to demonstrate the effectiveness of the approach
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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.

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Inagaki, Yasuyoshi, Katsuhiko Toyama, Nobuo Kawaguchi, Shigeki Matsubara, Satoru Matsunaga, 康善 稲垣, 勝彦 外山, 信夫 河口, 茂樹 松原, and 悟. 松永. "Sync/Mail : 話し言葉の漸進的変換に基づく即時応答インタフェース." 一般社団法人情報処理学会, 1998. http://hdl.handle.net/2237/15382.

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Karlsson, Kristina. "Semantic represenations of retrieved memory information depend on cue-modality." Thesis, Stockholms universitet, Psykologiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-58817.

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The semantic content (i.e., meaning of words) is the essence of retrieved autobiographical memories. In comparison to previous research, which has mainly focused on phenomenological experiences and age distribution of memory events, the present study provides a novel view on the retrieval of event information by addressing the semantic representation of memories. In the present study the semantic representation (i.e., word locations represented by vectors in a high dimensional space) of retrieved memory information were investigated, by analyzing the data with an automatic statistical algorithm. The experiment comprised a cued recall task, where participants were presented with unimodal (i.e., one sense modality) or multimodal (i.e., three sense modalities in conjunction) retrieval cues and asked to recall autobiographical memories. The memories were verbally narrated, recorded and transcribed to text. The semantic content of the memory narrations was analyzed with a semantic representation generated by latent semantic analysis (LSA). The results indicated that the semantic representation of visually evoked memories were most similar to the multimodally evoked memories, followed by auditorily and olfactorily evoked memories. By categorizing the semantic content into clusters, the present study also identified unique characteristics in the memory content across modalities.
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Slizovskaia, Olga. "Audio-visual deep learning methods for musical instrument classification and separation." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669963.

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In music perception, the information we receive from a visual system and audio system is often complementary. Moreover, visual perception plays an important role in the overall experience of being exposed to a music performance. This fact brings attention to machine learning methods that could combine audio and visual information for automatic music analysis. This thesis addresses two research problems: instrument classification and source separation in the context of music performance videos. A multimodal approach for each task is developed using deep learning techniques to train an encoded representation for each modality. For source separation, we also study two approaches conditioned on instrument labels and examine the influence that two extra sources of information have on separation performance compared with a conventional model. Another important aspect of this work is in the exploration of different fusion methods which allow for better multimodal integration of information sources from associated domains.
En la percepción musical, normalmente recibimos por nuestro sistema visual y por nuestro sistema auditivo informaciones complementarias. Además, la percepción visual juega un papel importante en nuestra experiencia integral ante una interpretación musical. Esta relación entre audio y visión ha incrementado el interés en métodos de aprendizaje automático capaces de combinar ambas modalidades para el análisis musical automático. Esta tesis se centra en dos problemas principales: la clasificación de instrumentos y la separación de fuentes en el contexto de videos musicales. Para cada uno de los problemas, se desarrolla un método multimodal utilizando técnicas de Deep Learning. Esto nos permite obtener -a través del aprendizaje- una representación codificada para cada modalidad. Además, para el problema de la separación de fuentes, también proponemos dos modelos condicionados a las etiquetas de los instrumentos, y examinamos la influencia que tienen dos fuentes de información extra en el rendimiento de la separación -comparándolas contra un modelo convencional-. Otro aspecto importante de este trabajo se basa en la exploración de diferentes modelos de fusión que permiten una mejor integración multimodal de fuentes de información de dominios asociados.
En la percepció visual, és habitual que rebem informacions complementàries des del nostres sistemes visual i auditiu. A més a més, la percepció visual té un paper molt important en la nostra experiència integral davant una interpretació musical. Aquesta relació entre àudio i visió ha fet créixer l'interès en mètodes d’aprenentatge automàtic capaços de combinar ambdues modalitats per l’anàlisi musical automàtic. Aquesta tesi se centra en dos problemes principals: la classificació d'instruments i la separació de fonts en el context dels vídeos musicals. Per a cadascú dels problemes, s'ha desenvolupat un mètode multimodal fent servir tècniques de Deep Learning. Això ens ha permès d'obtenir – gràcies a l’aprenentatge- una representació codificada per a cada modalitat. A més a més, en el cas del problema de separació de fonts, també proposem dos models condicionats a les etiquetes dels instruments, i examinem la influència que tenen dos fonts d’informació extra sobre el rendiment de la separació -tot comparant-les amb un model convencional-. Un altre aspecte d’aquest treball es basa en l’exploració de diferents models de fusió, els quals permeten una millor integració multimodal de fonts d'informació de dominis associats.
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Books on the topic "Multimodal Information Retrieval"

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Peters, 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.

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Jay, Kuo C. C., ed. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, Mass: Kluwer Academic Publishers, 2003.

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Li, Ying. Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation. Boston, MA: Springer US, 2003.

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C, 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.

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Forner, 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.

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Li, Ying. Video content analysis using multimodal information: For movie content extraction, indexing, and representation. Boston, MA: Kluwer Academic Publishers, 2003.

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Esposito, 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.

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Gosse, Bouma, and SpringerLink (Online service), eds. Interactive Multi-modal Question-Answering. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Andrzej, 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.

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Ute, Jekosch, Brewster Stephen 1967-, and SpringerLink (Online service), eds. Haptic and Audio Interaction Design: 4th International Conference, HAID 2009 Dresden, Germany, September 10-11, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Book chapters on the topic "Multimodal Information Retrieval"

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Baeza-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.

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Chang, 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.

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Marx, Jutta, and Stephan Roppel. "WING: An Intelligent Multimodal Interface for a Materials Information System." In 14th Information Retrieval Colloquium, 67–78. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3211-0_5.

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Alonso, Omar. "Crowdsourcing for Information Retrieval Experimentation and Evaluation." In Multilingual and Multimodal Information Access Evaluation, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23708-9_2.

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Bozzon, Alessandro, and Piero Fraternali. "Chapter 8: Multimedia and Multimodal Information Retrieval." In Search Computing, 135–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12310-8_8.

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Tautkute, Ivona, and Tomasz Trzciński. "SynthTriplet GAN: Synthetic Query Expansion for Multimodal Retrieval." In Neural Information Processing, 287–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92273-3_24.

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Mayer, Rudolf, and Andreas Rauber. "Multimodal Aspects of Music Retrieval: Audio, Song Lyrics – and Beyond?" In Advances in Music Information Retrieval, 333–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11674-2_15.

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Neumayer, Robert, and Andreas Rauber. "Multimodal Analysis of Text and Audio Features for Music Information Retrieval." In Multimodal Processing and Interaction, 1–17. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_11.

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Purificato, Erasmo, and Antonio M. Rinaldi. "A Multimodal Approach for Cultural Heritage Information Retrieval." In Computational Science and Its Applications – ICCSA 2018, 214–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95162-1_15.

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Rashid, Umer, Iftikhar Azim Niaz, and Muhammad Afzal Bhatti. "Unified Multimodal Search Framework for Multimedia Information Retrieval." In Advanced Techniques in Computing Sciences and Software Engineering, 129–36. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3660-5_22.

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Conference papers on the topic "Multimodal Information Retrieval"

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Ji, Wei, Yinwei Wei, Zhedong Zheng, Hao Fei, and Tat-seng Chua. "Deep Multimodal Learning for Information Retrieval." In MM '23: The 31st ACM International Conference on Multimedia. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3581783.3610949.

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Ahmed, Shaikh Riaz, Jian-Ping Li, Memon Muhammad Hammad, and Khan Asif. "Image segmentation approach in multimodal information retrieval." In 2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2013. http://dx.doi.org/10.1109/iccwamtip.2013.6716624.

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Zhang, Rui, and Ling Guan. "Multimodal image retrieval via bayesian information fusion." In 2009 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2009. http://dx.doi.org/10.1109/icme.2009.5202623.

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Bruno, Eric, Jana Kludas, and Stephane Marchand-Maillet. "Combining multimodal preferences for multimedia information retrieval." In the international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1290082.1290095.

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Rusnandi, Enang, Edi Winarko, and S. N. Azhari. "A Survey on Multimodal Information Retrieval Approach." In 2020 International Conference on Smart Technology and Applications (ICoSTA). IEEE, 2020. http://dx.doi.org/10.1109/icosta48221.2020.1570611095.

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Valstar, Michel, Tobias Baur, Angelo Cafaro, Alexandru Ghitulescu, Blaise Potard, Johannes Wagner, Elisabeth André, et al. "Ask Alice: an artificial retrieval of information agent." In ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2993148.2998535.

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Caicedo, Juan C. "Multimodal Information Spaces for Content-based Image Retrieval." In Third BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2009). BCS Learning & Development, 2009. http://dx.doi.org/10.14236/ewic/fdia2009.18.

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Furui, Sadaoki, and Koh'ichiro Yamaguchi. "Designing a multimodal dialogue system for information retrieval." In 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA: ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-84.

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Jin, Zhenkun, Xingshi Wan, Xin Nie, Xinlei Zhou, Yuanyuan Yi, and Gefei Zhou. "Ranking on Heterogeneous Manifold for Multimodal Information Retrieval." In 2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2023. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00162.

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Wajid, Mohd Anas, and Aasim Zafar. "Multimodal Information Access and Retrieval Notable Work and Milestones." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944581.

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