Relevant bibliographies by topics / Multimodal Information Retrieval

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multimodal Information Retrieval'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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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 (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, ret
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Cui, Chenhao, and Zhoujun Li. "Prompt-Enhanced Generation for Multimodal Open Question Answering." Electronics 13, no. 8 (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 depende
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Li, Peize, Qingyi Si, Peng Fu, Zheng Lin, and Yan Wang. "Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 4851–59. https://doi.org/10.1609/aaai.v39i5.32513.

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Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obt
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Yao, Ziru, and Fuzheng Zhao. "The Optimization of Media Information Retrieval and Adaptive Information Management Based on Deep Reinforcement Learning." Journal of Organizational and End User Computing 37, no. 1 (2025): 1–45. https://doi.org/10.4018/joeuc.378389.

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The rapid growth of large-scale information and the dynamic nature of user behaviors pose significant challenges for modern information retrieval systems, which often struggle to adapt to non-stationary environments and fail to fully utilize multimodal data, leading to suboptimal performance. To address these issues, this study proposes the adaptive deep reinforcement learning (RL) framework for information retrieval and management, which combines RL, multimodal data fusion, and an adaptive update mechanism to dynamically adjust to evolving user preferences and document collections. The adapti
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Mounika, Pilli, K. Venkata Subba Reddy, and N. Ramakrishnaiah. "Comprehensive review and analysis on multi modal image retrieval." Journal of Information and Optimization Sciences 46, no. 2 (2025): 403–13. https://doi.org/10.47974/jios-1923.

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Multimodal image retrieval, which involves retrieving images using various modalities such as text, audio, or other images, has significant research importance due to its wide-ranging applications and potential to enhance user experiences across multiple domains. Traditional image retrieval systems, content-based image retrieval (CBIR) systems rely solely on visual features, which can be limiting. By integrating multiple modalities, multimodal retrieval systems can offer more robust and accurate results. A research problem is considered multimodal when it integrates information from more than
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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 (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 techniqu
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Chen, Yali, Bin Hu, and Yajuan Liu. "Optimizing document management and retrieval with multimodal transformers and knowledge graphs." PLOS One 20, no. 6 (2025): e0323966. https://doi.org/10.1371/journal.pone.0323966.

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In the digital age, multimodal archival data is experiencing explosive growth, and how to efficiently and accurately retrieve information from it has become a key challenge. Traditional retrieval methods struggle to effectively handle multi-source heterogeneous multimodal data, leading to poor retrieval accuracy and efficiency. To address this issue, this paper proposes the MDKG-RL model, which organically integrates knowledge graph reasoning, deep reinforcement learning dynamic optimization, and multimodal Transformer architecture to achieve deep semantic understanding of multimodal data and
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Lang, Jian, Zhangtao Cheng, Ting Zhong, and Fan Zhou. "Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18035–43. https://doi.org/10.1609/aaai.v39i17.33984.

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Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MMTs) under missing modality conditions by applying learnable prompts. However, these prompt-based methods face several limitations: (1) incomplete modalities provide restricted modal cues for task-specific inference, (2) dummy imputation for missing content causes information loss and introduces noise, and (3) static prompts are instance-agnostic, offering limited knowledge for instances with various missing condition
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Pan, Xiaoning. "Application of Multimedia Information Retrieval Technology in Japanese Text Content Information Query Platform." International Journal of e-Collaboration 20, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijec.356491.

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The research and development of Japanese operating platform has become a new industry. At the same time, it is necessary to strengthen the research on query and feedback technology in MIR(Multimedia Information Retrieval). Based on this, this paper introduces the research status and progress of query and feedback technology in multimedia retrieval, and introduces the overall framework and technical links of MIR. This paper discusses the application of MIR technology in Japanese text content information query platform. A new multimodal retrieval mechanism is proposed, which combines cross-refer
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UbaidullahBokhari, Mohammad, and Faraz Hasan. "Multimodal Information Retrieval: Challenges and Future Trends." International Journal of Computer Applications 74, no. 14 (2013): 9–12. http://dx.doi.org/10.5120/12951-9967.

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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
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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 m
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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<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>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<br>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 d
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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<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação<br>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<br>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
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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
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Meseguer, Brocal Gabriel. "Multimodal analysis : informed content estimation and audio source separation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS111.

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Cette thèse propose l'étude de l'apprentissage multimodal dans le contexte de signaux musicaux. Tout au long de ce manuscrit, nous nous concentrerons sur l'interaction entre les signaux audio et les informations textuelles. Parmi les nombreuses sources de texte liées à la musique qui peuvent être utilisées (par exemple les critiques, les métadonnées ou les commentaires des réseaux sociaux), nous nous concentrerons sur les paroles. La voix chantée relie directement le signal audio et les informations textuelles d'une manière unique, combinant mélodie et paroles où une dimension linguistique com
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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 та ін. "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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Lerner, Paul. "Répondre aux questions visuelles à propos d'entités nommées." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG074.

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Cette thèse se positionne à l'intersection de plusieurs domaines de recherche, le traitement automatique des langues, la Recherche d'Information (RI) et la vision par ordinateur, qui se sont unifiés autour des méthodes d'apprentissage de représentation et de pré-entraînement. Dans ce contexte, nous avons défini et étudié une nouvelle tâche multimodale : répondre aux questions visuelles à propos d'entités nommées (KVQAE). Dans ce cadre, nous nous sommes particulièrement intéressés aux interactions cross-modales et aux différentes façons de représenter les entités nommées. Nous avons également é
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Books on the topic "Multimodal Information Retrieval"

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Peters, Carol, Valentin Jijkoun, Thomas Mandl, et al., eds. Advances in Multilingual and Multimodal Information Retrieval. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85760-0.

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Luo, Man, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, and Chitta Baral. Advances in Multimodal Information Retrieval and Generation. Springer International Publishing, 2025. http://dx.doi.org/10.1007/978-3-031-57816-8.

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

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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. 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. 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. Springer Berlin Heidelberg, 2011.

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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. Springer, 2008.

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David, Hutchison. Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music: 5th International Symposium, CMMR 2008 Copenhagen, Denmark, May 19-23, 2008 Revised Papers. 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. Springer Berlin Heidelberg, 2009.

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Gokhale, Tejas, and Yezhou Yang. Advances in Multimodal Information Retrieval and Generation. Springer International Publishing AG, 2024.

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Book chapters on the topic "Multimodal Information Retrieval"

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Luo, Man, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, and Chitta Baral. "Multimodal Information Retrieval." In Advances in Multimodal Information Retrieval and Generation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57816-8_3.

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Luo, Man, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, and Chitta Baral. "Multimodal Content Generation." In Advances in Multimodal Information Retrieval and Generation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57816-8_4.

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Baeza-Yates, Ricardo. "Retrieval Evaluation in Practice." In Multilingual and Multimodal Information Access Evaluation. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15998-5_2.

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Luo, Man, Tejas Gokhale, Neeraj Varshney, Yezhou Yang, and Chitta Baral. "Retrieval Augmented Modeling." In Advances in Multimodal Information Retrieval and Generation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57816-8_5.

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Chang, Edward Y. "Multimodal Fusion." In Foundations of Large-Scale Multimedia Information Management and Retrieval. 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. 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. 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. 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. 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. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11674-2_15.

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Conference papers on the topic "Multimodal Information Retrieval"

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Strand, Aleksander Theo, Sushant Gautam, Cise Midoglu, and Pål Halvorsen. "SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries." In 2024 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2024. https://doi.org/10.1109/cbmi62980.2024.10859209.

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Yohannes, Hailemariam Mehari, Yaqub Mahmoud, Mohammed Nazeeruddin, and Chaubey Dhananjay. "Multimodal Retrieval and Fusion Framework (MRaFF)." In 2025 8th International Conference on Information and Computer Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64582.2025.00035.

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Hadizadeh, Hadi, S. Faegheh Yeganli, Bahador Rashidi, and Ivan V. Bajić. "Mutual Information Analysis in Multimodal Learning Systems." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00068.

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Thiyagarajan, Kailash. "Multimodal RAG for Enhanced Information Retrieval and Generation in Retail." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961713.

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Zhao, Pengcheng. "Distill Align: Aligning Multimodal Features in Video Moment Retrieval." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064341.

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Dukia, Kanishk, Utsav Gupta, Vasudev Dehalwar, and Amit Kumar Nandanwar. "A Novel Multimodal Transformer Approach for Targeted Information Retrieval from Obscure Images." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10941154.

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Liu, Rex, and Xin Liu. "MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00048.

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Anand, Avinash, Raj Jaiswal, Abhishek Dharmadhikari, et al. "GeoVQA: A Comprehensive Multimodal Geometry Dataset for Secondary Education." In 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2024. http://dx.doi.org/10.1109/mipr62202.2024.00023.

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Luo, Yang, Zangwei Zheng, Zirui Zhu, and Yang You. "How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning?" In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.305.

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Wu, Shangda, Yashan Wang, Ruibin Yuan, et al. "CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.27.

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